CRITICAL THINKING IN DATA ANALYTICS
    9Jul, 2023
    Critical thinking and Why it’s important in Data Analytics ?

    What is Critical Thinking ?

    Critical thinking is a valuable skill in the field of data analytics as it helps analysts approach problems objectively, evaluate information, and make informed decisions based on data.

    • Critical thinking is the  ability to logically and carefully evaluate and analyze information, ideas, and opinions is referred to as critical thinking.
    • It involves challenging beliefs, taking into account many viewpoints, and reaching evidence-based conclusions.
    • When making judgments and resolving difficult issues, critical thinkers are able to spot biases, errors, and mistakes in thinking.
    • They have an open mind and are prepared to take into account different points of view, understanding that there may be more than one acceptable answer or interpretation.
    • Additionally, the ability to communicate successfully is made easier by having critical thinking abilities since it allows one to express one’s ideas convincingly.
    • Critical thinking skills are more important than ever in the quickly evolving world of today, when information is abundant but sometimes incorrect or unfair.
    • It gives people the ability to sort through the heap of false data and reach informed conclusions.
    • Furthermore, critical thinking encourages people to question accepted beliefs and consider other viewpoints, which promotes creativity and innovation.
    • It helps people to think creatively and provide original solutions to challenging issues.

     . In conclusion actively analyze and evaluate information, arguments, and evidence in a systematic and logical manner. It involves questioning assumptions, identifying biases, recognizing patterns, and making informed judgments based on reliable and relevant data.

    Benefits of Critical Thinking

    Improved problem-solving.

    Critical thinking develops systematically, manageable problem-solving skills through creativity, flexibility, and analytical reasoning.

    It allows individuals to approach challenges with an open mind, exploring multiple perspectives and considering various solutions.

    By encouraging curiosity and independent thought, critical thinking fosters innovation and adaptability. It enables individuals to evaluate information critically, distinguishing between reliable sources and misinformation.

    Moreover, critical thinking empowers individuals to make informed decisions based on evidence and logical reasoning. It equips them with the ability to identify biases and fallacies, ensuring that their judgments are objective and well-founded. Furthermore, critical thinking promotes effective communication skills, enabling individuals to articulate their thoughts clearly and persuasively.

    It also facilitates collaboration by encouraging active listening and respectful dialogue. In today’s complex world, where information is abundant yet often misleading, critical thinking is a vital skill that empowers individuals to navigate through uncertainty and make well-informed choices. 

    Enhanced decision-making:

    Critical thinkers evaluate evidence, consider multiple perspectives, and make informed judgments for better outcomes. They approach problems and situations with an open mind, questioning assumptions and seeking reliable information. By examining evidence from various sources, critical thinkers can identify biases and misinformation, allowing them to form well-rounded opinions. Moreover, they understand the importance of considering multiple perspectives, recognizing that there is rarely a single “right” answer. This ability to see different viewpoints fosters empathy and understanding, enabling critical thinkers to engage in productive discussions and find common ground. Ultimately, their informed judgments are based on a thorough analysis of the available evidence and a careful consideration of the potential consequences. By employing critical thinking skills, individuals can navigate complex issues more effectively and contribute to better outcomes in both personal and professional contexts.

    Knowing Self Abilities:

    Critical thinking fosters self-awareness by encouraging individuals to reflect on their own thoughts, beliefs, and biases. It helps them recognize their strengths and weaknesses, enabling them to make informed decisions based on their abilities. By understanding their limitations, individuals can effectively leverage their strengths and seek help or alternative perspectives in areas where they may lack expertise.

    Be more Open-Minded:

    Critical thinking promotes open-mindedness and intellectual humility. It encourages individuals to consider diverse viewpoints, challenge their own assumptions, and engage in respectful dialogue with others. By being open-minded, individuals become more receptive to new ideas, experiences, and perspectives, leading to a broader understanding of the world and more well-rounded decision-making.

    Able to Communicate Well:

    Critical thinking enhances communication skills by emphasizing the importance of clarity, coherence, and logical reasoning. When individuals think critically, they can articulate their thoughts, arguments, and ideas more effectively. They can also evaluate the credibility and validity of information, enabling them to communicate more persuasively and engage in constructive conversations.

    Increased Creativity:

    Critical thinking fuels creativity by encouraging individuals to explore multiple possibilities, think outside the box, and generate innovative solutions. It helps break away from conventional thinking patterns and encourages individuals to question assumptions, challenge norms, and seek alternative perspectives. By fostering creativity, critical thinking enables individuals to approach problems with fresh ideas and find unique solutions.

    Finding Ideas:

    Critical thinking facilitates idea generation by providing a structured framework for evaluating and synthesizing information. It helps individuals identify patterns, connections, and relationships between different concepts, allowing them to generate new ideas and insights. Critical thinking also helps individuals distinguish between valuable ideas and less effective ones, promoting a more focused and productive ideation process.

    Critical Thinking Characteristics

    Attitude

    • Critical thinking is characterized by a specific attitude or mindset that involves being intellectually curious, open-minded, and willing to question and challenge assumptions.
    • It requires individuals to approach situations with a desire for deeper understanding, actively seeking out information and considering alternative perspectives.
    • This mindset allows individuals to analyze and evaluate information objectively, weighing evidence and reasoning logically. Critical thinking also involves the ability to recognize and avoid biases or fallacies in one’s own thinking and the thinking of others. It is a valuable skill that can lead to more informed decision-making and problem-solving in various aspects of life.

    Criteria

    • Criteria are used in critical thinking to examine the relevance, dependability, accuracy, and validity of data, arguments, and evidence.
    • It enables people to differentiate between good and wrong reasoning, credible and incorrect sources, and powerful and weak arguments.
    • People can generate well-reasoned ideas, make well-informed judgments, and solve problems successfully by using their critical thinking abilities.
    • Additionally, critical thinking promotes intellectual independence and challenges beliefs and stereotypes, leading to a deeper understanding of complicated situations.

    Arguments:

    • Critical thinking involves the analysis and evaluation of arguments.
    • It requires individuals to examine the logical structure of an argument, identify its premises and conclusions, and assess the strength of the reasoning.
    • This includes evaluating the use of evidence, identifying logical fallacies or inconsistencies, and determining the overall coherence and persuasiveness of the argument.

    Evidence

    • Evidence is pointed out as being important in critical thinking when making statements or coming to conclusions.
    • People must look for and analyze relevant proof, taking into consideration its credibility, reliability, and source.
    • This includes identifying flaws or conflict of interest, differentiating between experiential and scientific proof, and assessing the quality of the evidence in connection to the argument or statement being made.

    How to Improve Critical Thinking

    Train Yourself to Think in Balance

    It’s crucial to develop a balanced way of thinking if you want to sharpen your critical thinking abilities. This involves evaluating the benefits and drawbacks of a scenario while taking into account other viewpoints. Avoid drawing quick conclusions or making decisions based only on feelings. Instead, before making a decision, gather relevant data, assess opposing opinions, and carefully evaluate the evidence.

    Example:

    Consider that you are considering whether to purchase stock in a specific business. If you wanted to think logically, you would do the following:

    Gather Information

    Begin by learning as much as you can about the business by doing some research. Look at the company’s financial health, market conditions, rivalry in the sector, and any most recent news or changes. Make sure you research a range of sources, taking into account both good and negative viewpoints.

    Evaluate Pros and Cons

    Make a list of the possible advantages and disadvantages of purchasing shares in the company. Think about things like the company’s track record, growth prospects, risks, and any obstacles it might face. When analyzing the arguments on any side, be truthful and neutral.

    Consider Alternative Viewpoints

    Look for various viewpoints and ideas on the investment. Read publications, consult specialists, or have a discussion with knowledgeable people about the subject. By doing so, you’ll be able to see the problem from an additional point of view and take into account opinions that might be opposed to your own.

    Consider your feelings and beliefs.

    Be aware of any preconceptions and emotions you may have that could affect how you make decisions. Consider whether you might be influenced by any personal preferences, prior assumptions, or feelings. Try to keep your emotions out of your logical assessment of the investment opportunity.

    Take Knowledgeable Action

    Make a well-informed choice on whether to buy the company’s stock based on your fair assessment. Keep in mind that no investment decision can be guaranteed to be fully risk-free because there are inherent risks in investing, even with a balanced approach.

    TRAIN YOURSELF TO THINK BALANCE

    Situational Awareness

    It takes observation and knowledge of the surroundings and details of a situation to develop situational awareness. Pay close attention to the details provided, the participants, and any deeper issues that might have an impact on the situation. You can better understand all aspects of a situation and arrive at more well-informed conclusions and decisions by keeping context in mind.

    Example 

    Imagine yourself alone and crossing an overcrowded street. You would take these actions to show situational awareness:

    Observe the Surroundings:

    Pay attention to your surroundings and take note of the people, buildings, vehicles, and any potential hazards or unusual activities. Be aware of the general atmosphere and any specific factors that may impact your safety or well-being.

    Identify Exit Routes:

    Mentally map out potential exit routes or safe areas in case of an emergency. Be aware of nearby buildings, open spaces, or crowded areas that could serve as options for seeking shelter or escaping if needed.

    Notice People’s Behavior:

    Observe the behavior of the people around you. Look for signs of agitation, aggression, or suspicious activity. Being aware of the behavior of others can help you anticipate and respond appropriately to any potential threats.

    Traffic Awareness:

    Be mindful of the flow of traffic, both pedestrian and vehicular. Watch for oncoming vehicles, traffic signals, and pedestrian crosswalks. Assess the traffic patterns and adjust your walking pace and direction accordingly to ensure your safety.

    Use Peripheral Vision:

    Expand your awareness beyond just what is directly in front of you. Utilize your peripheral vision to monitor activities happening in your periphery. This allows you to have a broader view of your surroundings and potential hazards that may not be immediately noticeable.

    CRITICAL THINKING FOUNDATION

    Develop Thinking Effectively & Efficiently

    Making your mental processes more effective and efficient is a way to improve thinking skills. You may achieve this by planning your ideas, breaking difficult issues into manageable pieces, and carefully analyzing each aspect. Use strategies like mind mapping, brainstorming, and outline creation to help organize your thoughts and ideas. To increase productivity, make an effort to reduce interruptions and focus on one activity or issue at a time.

    Example

    Say you have a challenging project at work that needs to be finished. You would do the following actions in order to successfully and efficiently build your thinking:

    Understand the Project Requirements:

    Start by carefully understanding the project’s requirements. All project briefings, instructions, and guidelines should be read. To make sure you understand what needs to be done, make notes and highlight important areas.

    Break Down the Project into Smaller Tasks:

    Make the project’s tasks easier to accomplish by breaking it up. Break down the important tasks or deadlines that must be met into smaller steps. This will assist you in developing a well-structured plan and method to successfully complete the job.

    Prioritize Tasks and Set Deadlines

    Evaluate each task’s urgency and priority. Rank them in order of importance to the overall success of the project. Give each task a reasonable deadline to help you stay focused and assure timely completion.

    Gather Relevant Information

    Do research and collect appropriate information to back up your project. Identify reliable resources that offer relevant information or data relevant to your project, such as books, papers, or reputable websites. Make well-organized notes and save your sources for later use.

    Generate Ideas and Develop Solutions

    use brainstorming (group discussion to produce ideas or solve problems) approaches to come up with concepts and potential answers for various project areas. Encourage original thought and experiment with various strategies. Consider the practicality of your ideas and how well they fit the project’s criteria before putting them in writing.

    Review and Revise:

    Regularly review your progress and assess the quality of your work. Take time to revise and refine your ideas, ensuring they meet the project requirements. Seek feedback from colleagues or supervisors to gain different perspectives and make necessary improvements.

     

    Active Listening

    ACTIVE LISTENING FOR CRITICAL THINKING

    Critical thinking can be enhanced by practicing active listening. It involves paying attention to both verbal and non-verbal indications while actively participating in conversations or presentations with others. Pay close attention to the speaker, get their explanation, and try to grasp their point of view. You can learn more, evaluate arguments more skillfully, and participate in more thoughtful and informed discussions by actively listening.

    Example :

    Imagine you are having a conversation with a friend who is sharing a personal experience. To demonstrate active listening, you would follow these steps:

    Give Your Full Attention:

    Make a conscious effort to give your full attention to your friend. Put away distractions, such as your phone or other devices, and focus on the conversation. Maintain eye contact and show genuine interest in what they are saying.

    Show Verbal and Non-Verbal Engagement:

    Use verbal cues, such as nodding your head, saying “yes,” or providing brief verbal affirmations to show that you are actively engaged in the conversation. Additionally, use non-verbal cues like facial expressions and body language to demonstrate attentiveness and understanding.

    Avoid Interruptions:

    Refrain from interrupting or interjecting your thoughts or opinions while your friend is speaking. Allow them to express themselves fully without interruption. Pause and give them time to finish their thoughts before responding.

    Paraphrase and Summarize:

    After your friend shares a point or completes a portion of their story, paraphrase or summarize what they said in your own words. This demonstrates that you are actively listening and understanding their perspective. It also provides an opportunity for clarification and ensures that you have correctly interpreted their message.

    Ask Relevant Questions:

    Ask thoughtful and open-ended questions to encourage your friend to elaborate on their experiences or thoughts. This shows that you are genuinely interested in understanding their perspective and encourages further discussion.

    Empathize and Validate:

    Express empathy and validate your friend’s feelings and experiences. Acknowledge their emotions and demonstrate that you understand and appreciate their point of view. This helps create a supportive and non-judgmental environment for the conversatio

    sql for data analysis
    6Jul, 2023
    Unlocking SQL: Why it’s Essential for Data Analytics in 2023 ?

    A language SQL (Structured Query Language) is used to manage and analyse relational databases. SQL can be used to retrieve, manipulate, and consolidate data for data analytics in order to get valuable insights.

    SQL has become a crucial tool in the field of data analytics in 2023. It is essential due to a number of important factors, including:

    Compatibility and Flexibility

    With both established relational databases and more recent innovations like cloud-based platforms, SQL is a widely used language that connects with many different database systems without any issues. Since data analysts may work with a variety of datasets thanks to this interoperability, SQL is still useful independent of the data architecture being used.

    Effective Data Recovery

    The efficient data retrieval from vast and complicated databases is made possible by SQL’s querying capabilities. Analysts can extract particular subsets of data, apply filtering criteria, merge data from other tables, and group data for aggregate computations with the help of the SELECT, WHERE, JOIN, and GROUP BY clauses. This effectiveness is essential for managing enormous amounts of data and quickly producing insightful findings.

    Powerful Data Transformation

    SQL offers a range of functions and operators to transform and manipulate data. Analysts can leverage CASE statements, string functions, and date/time functions to perform calculations, derive new columns, format data, and handle complex data transformations. These capabilities enhance the ability to preprocess and prepare data for analysis, ensuring its suitability for a wide range of analytical tasks.

    Aggregation and Summarization

    Data analytics often involve aggregating and summarizing data to extract valuable insights. SQL provides powerful aggregate functions like SUM, AVG, COUNT, MAX, and MIN, allowing analysts to calculate statistics, measure trends, and summarize data at different levels of granularity. The GROUP BY clause enables grouping data by specific dimensions, facilitating deeper analysis and reporting.

    Analytical Functions

    SQL’s expanding capabilities include analytical functions, such as window functions. These functions enable advanced calculations and analysis across subsets of data, making it easier to calculate running totals, rank data, perform time-based comparisons, and identify patterns within the dataset. Window functions enhance the analytical capabilities of SQL, making it a valuable asset for complex analysis scenarios.

    Integration with Business Intelligence (BI) Tools

    SQL provides a link between data storage and visualisation through seamless integration with a wide range of BI tools and platforms. To extract data and feed it directly into reporting and visualisation tools, analysts can use SQL queries. Because of this integration, data analysis can be done in real-time or very close to real-time, enabling organisations to act quickly on data. By using SQL, analysts can easily retrieve and manipulate data from different sources, including databases and data warehouses. They can then seamlessly transfer this data to BI tools such as Tableau or Power BI for visualization and reporting purposes. This integration not only streamlines the entire data analysis process but also enables organizations to gain valuable insights in real-time or near-real-time. With the ability to make data-driven decisions promptly, businesses can stay ahead of the competition and adapt to changing market trends effectively. SQL’s versatility in integrating with various BI platforms truly empowers organizations to unlock the full potential of their data and drive growth and success.

    Data Retrievalin SQL for Data Analytics

    Unlocking Insights Mastering Data Analytics in Excel
    29Jun, 2023
    Unlocking Insights: Mastering Data Analytics in Excel

    In today’s data-driven world, the ability to extract valuable insights from raw information is a highly sought-after skill. In the realm of data analytics, Excel stands out as a versatile and powerful tool for unlocking the true potential hidden within datasets. With its vast array of functions, formulas, and visualization capabilities, Excel empowers analysts and professionals to delve deep into their data, unravel patterns, and derive meaningful conclusions. From calculating averages and correlations to conducting advanced statistical analyses, Excel provides a solid foundation for mastering data analytics. This blog aims to guide readers on their journey of unlocking insights, exploring various techniques, tips, and tricks to maximize the potential of Excel for data analysis. By honing their skills and embracing the art of data analytics, readers can gain a competitive edge and unleash the full power of Excel to drive informed decision-making and uncover valuable business intelligencece

    Dataset for Excel Question in Analytics?

    excel question for data analytics

    Sample Questions for Analysis

    1. What is the average age of the respondents in the dataset?
    2. How many male and female respondents are there in the dataset?
    3. What is the highest income reported in the dataset?
    4. What is the most common education level among the respondents?
    5. Is there a correlation between age and satisfaction rating? If so, what is it?
    6. What is the median income of the respondents?
    7. How many respondents have a satisfaction rating of 7 or higher?
    8. What is the percentage of respondents with a Bachelor’s degree or higher?
    9. Is there a difference in satisfaction ratings between males and females?
    10. What is the average satisfaction rating for each education level?

    What is the average age of the respondents in the dataset?

    Formula: =AVERAGE(B2:B11)
    Answer: The average age of the respondents in the dataset is 39.2. This is calculated by using the AVERAGE formula on the range of ages (column B) in the dataset.

    How many male and female respondents are there in the dataset?

    Formula (for males): =COUNTIF(C2:C11, “Male”)
    Formula (for females): =COUNTIF(C2:C11, “Female”)
    Answer: There are 5 males and 5 females in the dataset. These counts are obtained by using the COUNTIF formula to count the occurrences of “Male” and “Female” in the gender column (column C).

    What is the highest income reported in the dataset?

    Formula: =MAX(D2:D11)
    Answer: The highest income reported in the dataset is 120,000. This is determined using the MAX formula on the income column (column D).

    Is there a correlation between age and satisfaction rating? If so, what is it?

    Formula: =CORREL(B2:B11, F2:F11)
    Answer: To determine if there is a correlation between age and satisfaction rating, we can calculate the correlation coefficient. The result is [correlation coefficient value]. If the correlation coefficient is close to 1 or -1, it indicates a strong positive or negative correlation respectively. If the correlation coefficient is close to 0, it indicates a weak correlation

    What is the median income of the respondents?

    =MEDIAN(D2:D11)
    Answer: The median income of the respondents is 67,500. The median represents the middle value when the incomes are arranged in ascending order. It is calculated using the MEDIAN formula on the income column (column D)

    How many respondents have a satisfaction rating of 7 or higher?

    Formula: =COUNTIF(F2:F11, “>=7”)
    Answer: There are 4 respondents who have a satisfaction rating of 7 or higher. This count is obtained using the COUNTIF formula to count the number of values in the satisfaction rating column (column F) that are greater than or equal to 7.

    What is the percentage of respondents with a Bachelor's degree or higher?

    Formula: =(COUNTIF(E2:E11, “Bachelor’s”)+COUNTIF(E2:E11, “Master’s”)+COUNTIF(E2:E11, “Doctorate”))/COUNTA(E2:E11)*100
    Answer: [result]% of the respondents have a Bachelor’s degree or higher. The formula counts the number of respondents with a Bachelor’s degree, Master’s degree, or Doctorate degree using the COUNTIF function, adds them together, divides by the total count of respondents, and multiplies by 100 to get the percentage.

    Is there a difference in satisfaction ratings between males and females?

    Formula (for males): =AVERAGEIF(C2:C11, “Male”, F2:F11)
    Formula (for females): =AVERAGEIF(C2:C11, “Female”, F2:F11)
    Answer: The average satisfaction rating for males is [result], and for females is [result]. By comparing these values, we can determine if there is a difference in satisfaction ratings between males and females. If the averages are significantly different, it suggests a potential difference in satisfaction levels between the two genders.

    What is the average satisfaction rating for each education level?

    or “High School” education level:
    =AVERAGEIF(E2:E16, “High School”, F2:F16)

    For “Bachelor’s” education level:
    =AVERAGEIF(E2:E16, “Bachelor’s”, F2:F16)

    For “Associate’s” education level:
    =AVERAGEIF(E2:E16, “Associate’s”, F2:F11)

    For “Master’s” education level:
    =AVERAGEIF(E2:E11, “Master’s”, F2:F11)

    For “Doctorate” education level:
    =AVERAGEIF(E2:E11, “Doctorate”, F2:F11)

    Replace the range E2:E11 with the actual range of the “Education Level” column in your dataset, and replace F2:F16 with the actual range of the “Satisfaction Rating” column in your dataset.

    By applying these formulas, you will get the average satisfaction rating for each education level.

    Sample Question for Data Analytics Excel

    excel for data analytics
    1. What is the total quantity sold across all categories?
    2. Which product has the highest price?
    3. How many products belong to Category 1?
    4. What is the average price of products in Category 2?
    5. Which category has the highest total price?
    6. What is the total value of all products sold?
    7. Which product has the lowest quantity?
    8. How many products are there in Category 3?
    9. What is the average quantity of products in Category 1?
    10. What is the total revenue generated from the sale of products in Category 2?

    What is the total quantity sold across all categories?

    The formula =SUM(D2:D11) calculates the sum of values in the Quantity column (D2:D11). It adds up all the quantities sold for each product and provides the total quantity sold across all categories.

    Which product has the highest price?

    The formula =INDEX(A2:A11, MATCH(MAX(C2:C11), C2:C11, 0)) identifies the maximum price in the Price column (C2:C11) using the MAX function. It then uses the MATCH function to find the row number of the maximum price. Finally, the INDEX function retrieves the corresponding product name from the Product column (A2:A11). This formula determines the product with the highest price.

    How many products belong to Category 1?

    =COUNTIF(B2:B11, “Category 1”) utilizes the COUNTIF function to count the number of occurrences of “Category 1” in the Category column (B2:B11). It provides the count of products that belong to Category

    What is the average price of products in Category 2?

    The formula =AVERAGEIF(B2:B11, “Category 2”, C2:C11) employs the AVERAGEIF function. It checks the Category column (B2:B11) for “Category 2” and calculates the average of the corresponding prices from the Price column (C2:C11). This formula determines the average price of products in Category 2.

    Which category has the highest total price?

    formula is

    Category 1=SUMIF(B2:B11,J7,Table_excel2[Price])
    Category 2=SUMIF(B3:B12,J8,Table_excel2[Price])
    Category 3=SUMIF(B4:B13,J9,Table_excel2[Price])

    out of this three category, we can find the highest total price

    What is the total value of all products sold?

    Formula: =SUMPRODUCT(C2:C11, D2:D11)

    Explanation: This formula uses the SUMPRODUCT function to calculate the total value of all products sold. It multiplies each price (C2:C11) by its corresponding quantity (D2:D11) and then sums up the resulting value

    Which product has the lowest quantity?

    Formula: =INDEX(A2:A11, MATCH(MIN(D2:D11), D2:D11, 0))

    Explanation: This formula uses the INDEX and MATCH functions to find the product name associated with the lowest quantity. MIN(D2:D11) finds the minimum quantity in the Quantity column (D2:D11), and MATCH(MIN(D2:D11), D2:D11, 0) identifies the row number of that minimum quantity. Finally, INDEX(A2:A11, MATCH(MIN(D2:D11), D2:D11, 0)) retrieves the corresponding product name from the Product column (A2:A11).

    How many products are there in Category 3?

    Formula: =COUNTIF(B2:B11, “Category 3”)

    What is the average quantity of products in Category 1?

    To calculate the average quantity of products in Category 1, you can use the following formula:

    Formula: =AVERAGEIF(Category, “Category 1”, Quantity)

    For example, if the Category column is in column B and the Quantity column is in column D, the formula would be:

    =AVERAGEIF(B2:B11, “Category 1”, D2:D11)

    This formula checks the Category column (B2:B11) for the value “Category 1” and calculates the average of the corresponding quantities from the Quantity column (D2:D11). It provides the average quantity of products in Category 1.

    Excel tricks for Data Analysis
    Entry level data analyst resume for freshers
    15Jun, 2023
    Top points for Entry level data analyst resume for freshers in 2023

    Contact Information

    Full Name:

    At the beginning of your resumes, provide your entire name. To make it stand out from the rest of the text, use a font size that is just a little larger.
     

    Phone Number:

    Give potential employers a trustworthy phone number to contact you at. Specify your country code if necessary.

    Email Address:

    Use a formal email address that begins with your name or a close variation of it. Do not use email addresses that are inappropriate or unprofessional.

    LinkedIn Profile URL:

    Include the URL to your complete, up-to-date LinkedIn profile, which should be included. Make sure your profile highlights your abilities, education, projects, and any pertinent employment history.

    Example:

    • Full Name: Manish Pandey
    • Phone Number: +91 9411776745
    • Email Address: manish.doe@email.com
    • LinkedIn Profile URL: www.linkedin.com/in/manish

    Remember to provide accurate and up-to-date contact information. Make sure your voicemail greeting is professional in case employers leave a message. Additionally, review your LinkedIn profile to ensure it presents you in a positive and professional light, as potential employers may view it to learn more about you.

    Don’t forget to adjust your objective statement to reflect the precise degree and certification you hold. Be sure to highlight your ability to analyse data, statistical analysis knowledge, and data manipulation and visualisation skills. Emphasise your dedication to utilise data-driven insights to inform decision-making and support an organization’s expansion.

     

    Objective

    As a beginning data analyst, you might highlight your professional objectives in the objective part of your resume. It need to reflect your passion for the subject matter and draw attention to your major qualifications. Keep the purpose brief and precise, and use powerful language to draw the reader in.

    Example 1.:

    Objective:

    Highly motivated and analytical recent graduate with a certification in Data analytics seeking an entry-level data analyst position to apply strong problem-solving and analytical skills. Eager to contribute to a dynamic team, utilize statistical analysis tools, and gain practical experience in data manipulation, visualization, and machine learning.

    example 2:

    Objective:

    Detail-oriented and analytical recent graduate with a Bachelor of Arts degree in Psychology and a certificate in Data Analytics from Vista Academy. Seeking an entry-level data analyst position to apply my strong analytical skills, knowledge of statistical analysis, and proficiency in data manipulation and visualization. Committed to leveraging data-driven insights to support decision-making and contribute to business growth.

    1. The applicant has a Bachelor of Arts in Psychology, demonstrating their comprehension of research techniques and human behaviour.
    2. They have a certificate in Data Analytics from Vista Academy, which attests to their advanced data analysis skills.
    3. They highlight their adeptness at manipulating and analysing data through their excellent analytical abilities, statistical analysis knowledge, and data manipulation and visualisation skills.
    4. They state their dedication to using data-driven insights to inform decisions and assist business expansion.

    Don’t forget to adjust your objective statement to reflect the precise degree and certification you hold. Be sure to highlight your ability to analyse data, statistical analysis knowledge, and data manipulation and visualisation skills. Emphasise your dedication to utilise data-driven insights to inform decision-making and support an organization’s expansion.

    Technical Skills:

    It’s crucial to emphasise your technical expertise with regard to data analysis and the technologies you are familiar with as a beginning data analyst. Employers can see your technical skills fast thanks to this part. Consider the following important technical skills:

    Statistical Analysis:

    Display your mastery of statistical analysis methods and tools, such as data modelling, regression analysis, and hypothesis testing. Mention any particular programming languages or statistical tools that you are familiar with, such as Python’s statistical libraries (such as NumPy and SciPy).

    Data Manipulation

    Draw attention to your data manipulation and cleaning skills by utilising tools like SQL (Structured Query Language) for database querying, filtering, and dataset joining. Include any knowledge you may have of data cleansing procedures, such as handling missing numbers, detecting outliers, and data transformation.

    Data Visualization:

    Showcase your skills in data visualisation to effectively convey findings. Mention any visualisation software you are knowledgeable about, such as Tableau, Power BI, or Python’s matplotlib. Talk about your capacity to design visually appealing charts, graphs, and dashboards that effectively and succinctly communicate complicated information.

    Programming

    Showcase your programming expertise, especially in languages like Python or R that are frequently used for data research. Showcase your knowledge of working with data structures, producing effective and clean code, and utilising libraries and packages for data analysis tasks.

    Database Management:

    Mention your expertise in database management systems (DBMS) like MySQL, PostgreSQL, or MongoDB if you have experience working with databases. Include your knowledge of designing and optimising database structures, writing SQL queries, and extracting data for analysis.

    Machine Learning

    Highlight your expertise of algorithms, model training, and evaluation if you have any experience or knowledge in machine learning. Mention any machine learning frameworks or libraries that you are familiar with, such as TensorFlow or Scikit-Learn.

    Example of Technical Skills section:

    Technical Skills:

    • Statistical Analysis: R, Python (NumPy, Pandas, scikit-learn)
      Data Manipulation:
    • SQL, Data cleaning techniques
    • Data Visualization: Tableau, matplotlib
      Programming:
    • python,
      Database Management:
    • MySQL, PostgreSQL
      Machine Learning: scikit-learn,

    Projects:

    The key to exhibiting your practical expertise and showcasing your capacity to apply data analysis approaches to real-world circumstances is to highlight your data analysis projects. Include any pertinent projects you’ve worked on throughout your studies, during internships, or as part of your own initiatives. How to present your projects is as follows:

    • Give each project a succinct title and clearly and succinctly summarise its goal or area of concentration.
    • What is the project? Give a brief summary of the project, mentioning the information used, the issue or question you sought to resolve, and any processes or strategies used.
    • Describe your position and duties: Describe the precise aspects of the project that you contributed to, such as data collecting, data cleaning and preprocessing, exploratory data analysis, statistical analysis, and data visualisation.
    • Describe the main conclusions or insights: Talk about any important conclusions or learnings you have from the analysis. Be sure to emphasise how these results helped to clarify the issue or provided useful advice.
    • Mention the equipment and methods employed: Include a list of the applications, programming languages, libraries, and frameworks that were used for the project. This demonstrates your familiarity with the equipment used often in the sector.
    • Mention any quantitative outcomes or results from your data analysis, such as greater accuracy, efficiency, or cost savings. If possible, quantify the results.

    Example of a Project section:

    Projects:

     

    Customer Segmentation Analysis
    • Used a dataset of customer demographics and purchase history to segment customers based on their buying behavior.
    • Conducted exploratory data analysis and applied clustering algorithms (k-means) to identify distinct customer segments.
    • Created visualizations to illustrate the characteristics and purchasing patterns of each segment.
    • Identified key insights, such as high-value customer groups and potential marketing strategies for each segment.
    • Tools Used: Python (NumPy, Pandas, scikit-learn), Tableau

    Sales Forecasting for a Retail Store
    • Analyzed historical sales data to develop a forecasting model for predicting future sales.
    • Preprocessed the data, performed time series analysis, and applied forecasting techniques (ARIMA) to generate predictions.
    • Evaluated the model’s performance using appropriate metrics and visualized the forecasted sales trends.
    • Provided actionable recommendations to optimize inventory management and staffing based on the forecasted sales.
    • Tools Used: R, SQL, Tableau


    Ensure that you select projects that demonstrate your proficiency in relevant data analysis techniques and tools. Focus on projects that highlight your ability to solve problems, work with real data, and draw meaningful insights. By showcasing your projects, you provide evidence of your practical skills and make your resume more impactful to potential employers.

    Educational Qualifications:

    Your school background is crucial in showing your foundation of knowledge and pertinent courses as a newcomer to the profession of data analysis. Here’s how to properly draw attention to your educational background:

    • your highest degree first: Mention your greatest level of education first, such as a Bachelor’s or Master’s degree, along with the subject area.
    • Add the institution’s name: Name the college or university where you received your degree.
    • Mention the degree’s name and its area of study: Mention the type of degree you have (e.g., Bachelor of Science, Master of Arts) as well as any concentrations or majors you may have had in that degree, such as Data Science, Statistics, Economics, or any other pertinent discipline.
    • Include the graduating year: Include the year you earned or anticipate earning your degree.

    Educational Qualifications:

    • Bachelor of Arts in Economics , XYZ University, 2023
    • Relevant Coursework: Data Analysis, Statistical Modeling, Machine Learning, Database Management
    • Certificate in Data Analytics, Vista Academy, 2023

    Keep in mind to concentrate on your educational background that is most pertinent to the position you are looking for. If you have earned any further qualifications or pertinent courses, you can list them in a separate section for professional development or certifications.

    By emphasising your educational background, you may show prospective employers that you have formal training in data analysis and the fundamental skills required to succeed in an entry-level data analyst position.

    Internships or Relevant Work Experience:

    Freshers might not have much professional experience, but including internships or any relevant work experience can really help your resume stand out. The following are some efficient ways to draw attention to your internships or equivalent professional experience:

    • Identify the company: Name the business or organisation where you finished your internship or obtained the necessary experience.
    • Tell us about your duties: List the main duties and responsibilities you carried out throughout your internship or professional experience. Concentrate on data analysis-related duties, such as gathering data, cleaning it up, manipulating it, performing statistical analysis, or visualising it.
    • Describe your accomplishments: Mention any particular successes or results you were able to attain throughout your internship or employment experience. This can entail finishing a data analysis project on schedule, streamlining the data analysis procedure, or drawing useful conclusions from the data.
    • Highlight the methods and tools used: Mention the applications, coding languages, or equipment you used during your internship or professional experience. This demonstrates your practical expertise and knowledge with tools used in the sector.

    Example of Internships or Relevant Work Experience section:

    • Internships or Relevant Work Experience:
      Data Analysis Intern, ABC Company, Summer 2022
    • Assisted in collecting and cleaning large datasets from various sources for analysis.
    • Conducted statistical analysis and performed data visualization to identify trends and patterns.
    • Created interactive dashboards to present data findings using Tableau.
      Collaborated with cross-functional teams to provide insights for business decision-making.

    Don’t forget to emphasize any internships or employment that demonstrates transferable abilities like analytical thinking, problem-solving, or teamwork or is directly related to data analysis. If you have experience, highlight any duties or projects that show you can effectively work with data or analyse information, even if that experience isn’t particularly in data analysis.

    Relevant Coursework

    To demonstrate your knowledge and expertise in the industry, it’s essential that you give attention to any coursework or academic projects that are directly related to data analysis. Here are some tips for properly presenting your pertinent schoolwork or academic projects:

    List relevant courses

    Mention the classes you’ve taken that have a direct bearing on data analysis. Classes on statistics, data management, data visualisation, machine learning, or any other pertinent topics may fall under this category.

    Highlight key projects

    Mention any noteworthy tasks you have finished as part of your coursework. Describe the project’s goals, techniques, data analysis, and any noteworthy conclusions or insights that emerged from it.

    Showcase technical skills:

    The technical talents you developed or used for your coursework or academic projects should be highlighted. This might include tools for data visualisation, statistical software, database management systems, or computer languages.

    Demonstrate teamwork or collaboration

    Mention how well you can communicate your ideas, work well in a team, and contribute to the successful completion of tasks, especially if you have worked on group projects or cooperated with peers.

    Example of Relevant Coursework or Academic Projects section:

    Relevant Coursework or Academic Projects:
    Data Analysis and Visualization
    • Completed a project analyzing a real-world dataset using Python and generating visualizations to identify trends and patterns.
    Statistical Methods for Data Analysis
    • Conducted a comprehensive analysis of survey data using statistical techniques such as regression analysis and hypothesis testing.
    Database Management Systems
    • Designed and implemented a relational database to store and retrieve data efficiently using SQL.

    Remember to focus on coursework or academic projects that directly relate to data analysis and highlight the skills and knowledge you gained through those experiences. This section allows you to showcase your academic achievements and your ability to apply data analysis concepts in practical scenarios.

    By highlighting relevant coursework or academic projects, you demonstrate to potential employers that you have acquired a strong foundation in data analysis principles and have practical experience working with data.

    Data Analysis Tools

    An entry-level data analyst must be proficient in data analysis applications and tools. You can demonstrate your familiarity with tools used in the industry and your proficiency with data in this part. Here’s how to demonstrate your knowledge of data analysis applications and tools:

    List the tools and software


    Mention the specific software and tools for data analysis with which you are familiar. Programming languages, statistical programmes, database management systems, data visualisation tools, and any other pertinent software may fall under this category.


    Rate your proficiency:


    Give each tool or piece of software a proficiency rating, such as “Proficient,” “Intermediate,” or “Advanced.” Employers can see what your expertise is through this.


    Provide examples


    Mention them as examples to demonstrate your practical application of these technologies if you have finished assignments or coursework utilising certain software or applications.


    Example of Data Analysis Tools
    • Python (NumPy, Pandas, scikit-learn): Proficient
    • Excel:Advance
    • SQL: Proficient
    • Power Bi: Intermediate


    You may prove that you are capable of working with a variety of technologies by displaying your skill with data analysis tools and applications. Your credibility as a beginning data analyst will be enhanced, and you’ll have a better chance of getting hired.

    Analytical and Problem-Solving Skills:

    Employers seek applicants with the analytical and problem-solving skills necessary for a data analyst position, as well as the ability to efficiently analyse data and produce insightful conclusions. How to demonstrate your capacity for analysis and problem-solving

    Provide specific examples:


    Mention examples of when you’ve worked on challenging projects that called for critical thought and problem-solving abilities or addressed analytical difficulties successfully. Projects for school, internships, or independent work all qualify.


    Describe your approach


    Give a brief description of your process for tackling analytical difficulties. Emphasise your capacity for dissecting complex issues into smaller, more manageable parts and your expertise in the use of analytical methods and tools to draw conclusions from data.


    Demonstrate attention to detail


    Emphasize your attention to detail in data analysis tasks. Discuss your ability to identify patterns, outliers, or inconsistencies in data, and how you ensure data accuracy and integrity.


    Showcase critical thinking:


    Highlight your ability to think critically and make logical connections between data points or variables. Discuss your capability to analyze data from multiple angles, consider alternative hypotheses, and draw well-supported conclusions.


    Example of Analytical and Problem-Solving Skills section:


    Analytical and Problem-Solving Skills:
    • Successfully analyzed a large dataset of customer feedback to identify recurring themes and patterns, leading to actionable recommendations for improving customer satisfaction.
    • Employed statistical analysis techniques to uncover relationships between product features and customer preferences, enabling data-driven decision-making in product development.
    • Demonstrated strong attention to detail by identifying data anomalies and conducting rigorous quality checks to ensure data accuracy.
    communication for data analytics

    Communication and Data Visualization Skills:

    For a data analyst to communicate complicated information in a way that is understandable and straightforward, effective communication and data visualisation abilities are essential. How to demonstrate your data visualisation and communication abilities:


    Present data visualization examples


    Give examples of the data visualisations you’ve produced to clearly convey findings. You may have created charts, graphs, dashboards, or presentations for your studies, internships, or independent work.


    Describe your ability to simplify complex concepts


    Emphasize your capability to present complex analytical findings in a concise and understandable manner. Discuss how you have effectively communicated technical information to non-technical stakeholders or team members.


    Highlight your written communication skills:


    Mention any reports, records, or summaries you’ve created to clearly communicate analytical results. This demonstrates your capacity for clear thought and written information presentation.


    Discuss your teamwork and collaboration skills


    Data analysts frequently collaborate with different stakeholders and operate in teams. Emphasise your prior experience collaborating with others, communicating effectively, and participating in team initiatives.


    Example of Communication and Data Visualization Skills section:

     

    Communication and Data Visualization Skills:
    • Created interactive dashboards using Tableau to present complex data analysis findings in a visually appealing and easy-to-understand format.
    • Prepared comprehensive reports summarizing analytical insights and actionable recommendations for senior management.
    • Effectively communicated analytical findings to cross-functional teams, including non-technical stakeholders, resulting in improved decision-making processes.\Collaborated with team members to analyze data collectively and deliver impactful presentations on project outcomes.

    Resume Sample

    resume example data analytics
    The Best Data Analytics Institute In Dehradun
    Reasons why skills are becoming more important in 2023
    8Jun, 2023
    Top Data Analytics beginner Jobs An overview 2023

    You want to work as a data analyst, then. You’ve done extensive study and come to the conclusion that it’s the right career for you. Alternatively, you may keep hearing about this exciting job title and want to find out more.

    In either case, you want to fully understand what working as a data analyst involves and, even more important, how to go about doing it.

    You may be interested in learning more about the market for new and aspiring data analysts in 2023 after experiencing a global epidemic over the past two years.

    Do employers still need data analysts?

    You’re in the correct place, I suppose. Consider this article to be your comprehensive guide to becoming a data analyst.

    What will the data analytics market look like in 2023?

    Is it a wise career decision to become a data analyst, if that’s what you’re considering? Even without a global epidemic, that is a challenging question to answer. You’re right to be inquisitive (and possibly anxious) about what 2023 has in store because for many, the previous two years have been fairly awful.

    So, what can you anticipate for the coming year? Let’s look at it.

     

    What will the data analytics market look like in 2023?

    Consider the larger context in which you’ll be working while thinking about a job as a data analyst. Every day, as people, we produce enormous amounts of data that are attractive to businesses and organisations because it provides them with information about how we behave in respect to their goods and services. Data generation increases as we rely more on digital tools and services, making it crucial for businesses to understand this data.

    The big data market is expected to be valued $229.4 billion USD by 2025, to give you an indication of its size. How does that affect you? Well, as the demand for data analysts rises dramatically, so does the market for data.

    This idea is supported by the Jobs of Tomorrow study from the World Economic Forum (released in 2020), which lists data science and artificial intelligence as two of the seven high-growth emerging professions. The largest growth rate of the seven recognised vocations, at 41% annually, is shown by data and AI. Currently, there aren’t enough data specialists to fulfil this need; a recent research found that companies anticipate data science and analytics to be one of the hardest fields to fill.

    Does remote work impact careers for data analysts?

    Without taking into account the recent past, it is impossible to discuss the data industry in 2023. The data sector has also seen significant transformation as a result of the pandemic.

    The rise of remote work, which has expanded by 300% since the epidemic, is one of the biggest and most evident changes. If the epidemic persists in 2023, you should plan on working remotely at least occasionally if not full-time as a data analyst. You’ll be in a better position to convince companies that you can operate independently and remotely if you’re learning data analytics through an online programme.

    How can a newly certified analyst like you stand out?

    Continuously Enhance Your Skills


    Keep up with the newest data analytics tools, technologies, and approaches. Through online courses, workshops, certifications, and attendance at industry conferences, always learn new things and improve your abilities. Develop your skills in areas like SQL, machine learning, statistical analysis, Python, R, and other computer languages.


    Showcase Your Technical Abilities:


    Showcase your expertise with industry-standard data analytics tools and platforms, such as Tableau, Power BI, or Python packages like Pandas and NumPy. In interviews and on your CV, be sure to emphasise your technical expertise. You should also include examples of projects or analyses where you used your technical expertise well.


    Emphasize Domain Knowledge


    Gain a thorough understanding of the field or sector you are working in. Whether you work in marketing, finance, healthcare, or another industry, having domain-specific expertise can enable you to offer more insightful analysis and suggestions. Keep up with the latest market developments, difficulties, and rules.


    Develop Strong Communication Skills


    As an analyst, it’s essential to be able to express your conclusions and observations clearly. Build your writing and spoken communication abilities to express complicated ideas succinctly and clearly. To effectively communicate your insight to stakeholders, practise developing visually appealing and informative data visualisations and presentations.


    Be a Problem Solve


    Consider how you can solve problems while you go about your work. Recognise the goals and issues facing the company, and concentrate on using data analysis to offer workable answers. Seek out opportunities to proactively identify and address problems or areas that need development, and show how your analytical talents can benefit the organisation.


    Collaborate and Network


    Engage in active collaboration with colleagues to gain knowledge from their viewpoints and experiences, both inside and outside of your team. Making connections with industry experts can open up new doors and opportunities. Expand your professional network by attending industry events, signing up for associations, taking part in online forums, and having talks.

    Use your status as a “newcomer” to your advantage. Even while the job market may be particularly competitive right now, you as a novice to the industry have many distinct insights and advantages. In your applications, be sure to draw attention to these. You can get assistance with this from a career coach, so pick a course that provides it. Here, you may do top data analytics certification courses.

    What kind of salary can data analysts in India expect in 2023?

    Data analysts’ salaries in India can vary depending on their level of expertise, region, industry, and size of their employer. However, based on past data and patterns, I can provide you some broad views on the predicted compensation range for data analysts in India. Note that these numbers are approximations and subject to change:

    Entry-level Data

    Analyst: For those with less than 2 years of experience, the salary range can vary from around INR 3.5 lakh to INR 6 lakh per annum.

    Mid-level Data Analyst:

    With 2 to 5 years of experience, data analysts can expect a salary range of approximately INR 6 lakh to INR 12 lakh per annum.

    Senior Data Analyst:

    Professionals with 5 or more years of experience may earn a salary ranging from INR 12 lakh to INR 20 lakh per annum or higher, depending on their expertise and responsibilities.

    It’s important to note that these figures are approximate and can vary significantly based on factors such as the candidate’s qualifications, skills, industry demand, company size, and location. Salaries tend to be higher in metropolitan cities like Mumbai, Bangalore, and Delhi compared to smaller cities or rural areas.

    Additionally, it’s worth mentioning that the salary ranges provided here are for data analysts specifically. Data scientists or professionals with more advanced skills and experience in areas such as machine learning and AI may command higher salaries.

    To get the most accurate and up-to-date salary information, it is advisable to refer to industry surveys, job portals, and consult with professionals working in the field or a human resources department.

    2. What does a data analyst do?

    29May, 2023
    Data science course and Data analytics course 2023

    Working with data to acquire insights and make decisions involves both the professions of data science and data analytics. Even while they have certain things in common, they also have different goals and skill sets. Here is a quick summary of each field and some courses you can think considering taking to learn more about them:

    Data Science:

    Using diverse methods, such as statistical analysis, machine learning, and data visualisation, data science aims to derive information and insights from data. It includes a wider range of abilities, such as programming, math, and subject-matter expertise. Data scientists generally use models and algorithms to analyse and interpret data as they work on challenging challenges.

    Here are some data science courses you might want to think about taking:

    • Data Science and Machine Learning Overview
    • Data Science Statistical Analysis and Modelling
    • Data Visualisation and Communication
    • Deep Learning, Big Data Analytics, and Neural Networks

    Data analytics:

    Data analytics is the process of identifying, extracting, and analysing patterns and insights from data to inform decisions and address business issues. In order to analyse large data sets and offer practical insights, it requires using statistical methods, data visualisation techniques, and data mining technologies. To respond to particular business inquiries, data analysts use both structured and unstructured data.

    The following courses might be of interest to you if you’re interested in learning data analytics:

    • Basics of Data Analytics Cleaning and Wrangling of Data
    • Analytics Data Visualisation
    • Business Intelligence and Reporting
    • Predictive Analytics Techniques Data Mining

    Data analytics vs Data science

     

    Data Analytics

    Data Science

    Focus and Scope

    The primary focus of data analytics courses is on analysing and interpreting data to find patterns and insights for smart business decisions.

    A broader range of subjects are covered in data science courses, including data analysis, machine learning, statistical modelling, and programming to create predictive models and algorithms.

    Skills Emphasized

    Data cleaning, data visualisation, descriptive statistics, business intelligence, and reporting are among the skills that are emphasised in data analytics courses.

    Statistical analysis, machine learning, programming (using languages like Python or R), data mining, data visualisation, and big data technologies are some of the skills that are presented in data science courses.

    Depth of Statistics and Mathematics:

    The core statistical and mathematical ideas required for descriptive analysis and comprehending data trends are often covered in data analytics courses.

    Advanced mathematics (linear algebra, calculus, probability) and inferential statistics for predictive modelling and hypothesis testing are covered in-depth in data science courses.

    The difficulty of algorithms

    Courses on data analytics may not address the creation of complicated algorithms, instead focusing on the application of already-developed algorithms and methodologies to analyze data and produce insights.

    The creation and application of complex algorithms, such as machine learning, deep learning, and optimization methods, is covered in data science courses.

    SOFTWARE AND TOOLS

    Data analysis, visualisation, and reporting technologies like Excel, Tableau, or Power BI may be the main focus of data analytics courses.

    Using tools like Jupyter Notebook, scikit-learn, TensorFlow, or PyTorch for data manipulation, modelling, and analysis is a common assignment in data science courses.

    Predictive Modeling:

    Although basic predictive modelling strategies may be briefly discussed in data analytics courses, the emphasis is largely on descriptive analysis and comprehending historical data.

    Big data technologies (like Hadoop and Spark) and data engineering ideas like data preparation, data pipelines, and distributed computing for managing massive datasets are covered in data science courses.

    Experimental Design and A/B Testing:

    Although A/B testing and rigorous experimental procedures may not be covered, basic experimental design concepts may be included in data analytics courses.

    Principles like experimental design and statistical testing, such as A/B testing, hypothesis testing, and experimental validation, are discussed in data science courses.

    Domain expertise

    Applying data analysis methods to certain fields, such as marketing, finance, or healthcare, may be the main focus of data analytics courses.

    Domain-specific applications may be covered in data science courses, but they also place an emphasis on a wider range of problem-solving abilities that are applicable to many different domains.

    Job Roles and Titles:

    Graduates of data analytics programmes frequently go on to hold positions with titles like business analyst, data analyst, or business intelligence analyst.

    Graduates of data science programmes frequently seek positions with titles like data scientist, machine learning engineer, or data engineer.

    Communication and Storytelling:

    Though communication skills are discussed in data analytics courses, the emphasis is typically on delivering analytical conclusions to business audiences.

    The capacity to successfully communicate complicated discoveries and insights gained from data to multiple stakeholders is frequently stressed in data science courses.

    Industry Applications:

    Data analytics courses may place more of an emphasis on particular business applications and domains, such as supply chain analytics, financial analysis, and marketing analytics.

    Numerous sector applications are covered in data science courses, including those in banking, healthcare, marketing, e-commerce, and social media research.

    Time Series Analysis

    Basic time series analysis principles may be included in data analytics classes, but advanced approaches are often not covered in as much detail.

    Time series analysis methods like forecasting, trend analysis, seasonality, and anomaly detection are frequently covered in-depth in data science courses.

    Database Systems

    Although query languages and database administration are typically not as heavily stressed in data analytics courses, they may touch on database systems.

    SQL queries, database management principles, and other techniques for effectively retrieving and manipulating data may be covered in data science courses.

    Advanced Analytics Platforms

    The use of commonplace analytics tools and platforms like Excel, Tableau, or Power BI may be the main focus of data analytics courses.

    Advanced analytics tools and frameworks for managing large-scale data processing and machine learning tasks, like Apache Spark or H2O.ai, may be introduced in data science courses.

    Optimization Techniques

    In comparison to data science courses, data analytics courses typically do not cover optimisation techniques as extensively.

    Optimisation strategies like linear programming, integer programming, and restricted optimisation are covered in data science courses and are helpful for solving challenging business challenges.

    Similarity between Data Analytics and Data science

    Data Analysis

    The examination of data to derive relevant insights and create data-driven decisions is a component of both data analytics and data science. Both disciplines seek to find patterns, trends, correlations, and connections in the data.

    Visualizing data

    Effective data visualisation is highlighted by both data analytics and data science. Charts, graphs, and dashboards are examples of visualizations that are used in both sectors to concisely convey insights and conclusions to stakeholders.

    Statistics Concepts:

    Statistics are used to analyse and interpret data in both data science and data analytics. Both fields frequently make use of ideas like descriptive statistics, hypothesis testing, regression analysis, and probability theory.

    Data preparation

    Data preprocessing procedures like data cleansing, data transformation, and data integration are used in both data analytics and data science. Both fields need treating outliers, handle missing values, and getting the data ready for analysis.

    Business Understanding:

    A complete understanding of the business context in which the data analysis is being conducted is necessary for both data analytics and data science. Insights that can guide decision-making and increase commercial value are looked after by both industries.

    Resolving problems

    Data science and data analytics both require problem-solving abilities. The capacity to recognize and categorize commercial problems, develop analytical strategies, and choose the proper methodologies and algorithms to address such problems is necessary in both professions.

    programming abilities:

    Both data analytics and data science require some level of programming expertise, however the focus may differ. For manipulating and analyzing data, data analysts frequently use technologies like SQL, Excel, or scripting languages. For data processing, modeling, and algorithm development, data scientists often use programming languages like Python or R.

    Expertise in the domain

    Having domain expertise in the particular sector or field they are working in benefits both data analytics and data science. Analysts and scientists are better able to pose pertinent questions, assess findings, and offer practical insights when they are aware of the context and particulars of the subject.

    Making Decisions Based on Data:

    Data-driven insights are intended to enhance decision-making processes in both data analytics and data science. By offering impartial and quantitative data to assist strategic and operational decisions, both professions contribute to evidence-based decision making.

    communication is a very important skill in Data Analytics
    28Apr, 2023
    Why communication is a very important skill in Data Analytics

    Data Analytics usually find insight and patterns from data and these outcomes are hard and complex to understand for non-It persons therefore It is essential for you to have the capabilities and skills to interpret data and deliver well-contained stories. As a data analyst, writing, presenting, and communicating, in general, is essential.

    Besides having skills like logical and problem-solving skills communication is a fundamental skill for data Analytics.

    There are several types of communication skills that Data analytics have to master we will discuss them in this article.

    Working and collaborating with team members and stakeholders

    You’ll spend a lot of time alone at a computer as a data analyst or data scientist studying datasets and algorithms. However, you’ll also certainly do a lot of teamwork. Working as a team with other analysts or scientists is typical, particularly when working on significant projects or managing enormous datasets. In addition, you’ll regularly collaborate with groups of individuals and stakeholders who don’t use data. To work well with others, it’s critical to have strong communication skills.

    Types of Skills Required in Data Analytics

    Some of the most recommendable Communication Skills Data Analytics must-have.

    Story Telling :

    Storytelling skills are especially critical in roles that involve marketing to an audience or sharing information with others. A good story can persuade, inform or entertain others depending on what kind of story you tell.

    The most important aspect of delivering data insights is storytelling. You will certainly employ storytelling tactics to deliver your presentations if you are an effective presenter. Additionally, while data analysis can be quite difficult, a strong plot will make the results useful and appealing to business users.

    Presenting Skills:

    Presentation skills help create innovative ideas when students come up with creative and interesting slides to illustrate their talk. The use of presentation aids makes for a much more interesting talk, and the creation of such aids can help develop students’ confidence.

    Data Visualization Skills:

    Data visualization is the representation of information and data using visuals such as graphs, charts, maps, and more. Those who use data visualization in their work use tools to present data to non-technical audiences in a form of storytelling that helps businesses make data-driven decisions.

    Critical Thinking :

    Critical thinking is the most demanded skill it is the ability to analyze facts and form a judgment. It is a form of emotional intelligence. Someone with critical thinking skills can think clearly and rationally when the situation demands it.
    It allows them to perform problem-solving and decision-making more effectively.

    Listening Skills:

    Listening is the ability to accurately receive and interpret messages in the communication process. • Listening is key to all effective communication, without. the ability to listen effectively messages are easily misunderstood. • Listening is one of the most important skills you can have.

     

    Keeping Audiences Engaged with Data Presentation

    There are certain point keep in mind before presenting your Data.

    Why they should Listen:

    If there isn’t a clear interest on behalf of your audience, you must convince them to pay attention. Because some audiences don’t interact openly, this can be awkward. The response? Give them a reason to care.

    For example, If you’re giving a talk about sales and marketing, remind the audience about a few salespeople who overcome extraordinary obstacles and succeeded.

    Stick to your Topic of discussion;

    Try to be specific and tell examples and stories to elaborate your finding and understand your audience requirement and what they want from you

    Make sure there is ‘flow’ in your presentation.

    If your presentation is compelling and audience members are hanging on your every word, then you’re in a state of ‘flow’.

    Story Telling

    There should be a few stories thrown throughout every presentation. The moment you say, “Let me tell you a story about…”, people start to pay attention because they are programmed to do so. However, be sure that the stories support the points you’re making.

    Let your audience talk

    Inviting audience members to speak will make them feel valued. In addition, it allows you to mentally rest for a little while.

    upskill yourself otherwise you will be replace
    19Apr, 2023
    Upskill Yourself otherwise you will be replace Data Analytics

    To stay relevant, you must upskill.

    Upskilling oneself is a crucial concept for both humans and AI. It is crucial to always learn new things and broaden our skill sets in order to remain relevant and competitive in the fast-paced, constantly changing world of today.

    Humans may upskill in a variety of ways, including by taking classes or attending workshops, reading books and articles, or even just looking for new challenges and experiences. People may improve their employability and career prospects while also helping their organisations succeed by constantly learning and growing.

    Data Analytics upskilling

    The discipline of data analytics is expanding and changing quickly due to the rising availability of data and the necessity to base choices on that data. Upskilling is crucial for those in this area to grow professionally as well as to remain competitive and relevant in a labour market that is continually changing.

    There are several ways for people to improve their data analytics skills. Taking classes or going to seminars is one of the best methods to learn new tools and skills. There are several online learning environments and academic institutions that provide data analytics courses, including subjects like data mining, machine learning, and data visualisation.

    1. In order to be competitive and relevant in the job market, those working in the constantly expanding sector of data analytics need to continually advance their skills.
    2. Developing new skills in data analytics may require taking classes, going to seminars, staying current on trends and technology, getting hands-on experience, and honing soft skills like communication and critical thinking.
    3. A person’s employability, career prospects, and readiness to fulfil employer demands can all be improved by upskilling in data analytics.
    4. People who don’t upgrade their data analytics abilities may be at risk of being replaced by others who have the knowledge and experience needed to thrive in this industry.
    5. Last but not least, it’s critical to keep in mind that upskilling calls for a lifetime dedication to learning and development. People may prosper in the quickly evolving field of data analytics and beyond by adopting this approach.

    The Effects of Not Upskilling in Data Analytics

    1. It’s crucial to upgrade your data analytics skills if you want to compete and stay relevant in the employment market.

    2. Finding career chances may become challenging if you don’t upgrade your data analytics skills.

    3. People who don’t stay current with data analytics advancements risk remaining in low-level, regular positions.

    4. Feelings of irritation, disappointment, and stagnation may result from this.

    5. Inadequate data analytics training may potentially have larger societal repercussions.

    6. People who are unable to use data efficiently may be unable to participate in crucial discussions and decision-making processes.

    7. As a result, perceptions may become more constrained and lack diversity.

    8. This ultimately threatens our capacity to solve challenging issues and reach wise judgements as a group.

    9. The repercussions of lagging in data analytics are too severe to be ignored.

    10. To stay relevant and competitive in the employment market, people must take proactive measures to stay abreast of the most recent advancements in data analytics.

    Why Soft Skills Matter in Data Analytics

    The fact that data analytics work frequently requires close collaboration is one of the main reasons soft skills are important. Data analysts regularly collaborate with people from a variety of professions and life experiences. They need to be able to collaborate to resolve challenging issues and communicate in a clear and convincing manner in order to be effective.

    Soft skills may also aid data analysts in better comprehending and empathising with the individuals they work with. To comprehend the wants and preferences of the customer, for instance, a data analyst working on a project involving customer satisfaction may need to be able to put oneself in the customer’s shoes. Soft abilities like as emotional intelligence and empathy may be quite useful in this sort of situation.

    The ethical and social implications of data analysts’ job may also be negotiated with the use of soft skills. Data analysts must be able to critically consider the ramifications of their work and convey the possible dangers and advantages to stakeholders as data plays a larger role in changing business and society.

    How to Improve Soft Skills in Analytics for Data

    1. Ask for feedback: Be open to constructive criticism and ask your coworkers, mentors, or bosses for their opinions on your soft skills.
    2. Take classes: There are several educational programmes that provide instruction in soft skills including leadership, communication, and emotional quotient.
    3. Active listening is a skill that can be developed. When speaking with people, make an effort to listen intently and provide intelligent questions.
    4. Collaborate: Look for opportunities to work with others who have a variety of backgrounds and areas of expertise, and try to comprehend their viewpoints and beliefs.
    5. Attempt to put yourself in the position of the individuals you are working with, whether they be coworkers, clients, or other stakeholders. This will help you develop empathy.
    6. Always be curious Keep an open mind to new information and concepts, and look for chances to broaden your knowledge and skill set.
    What Are Wide Range Of Application Of Data Analytics In 2023
    Range Function In Python Python Range Function With Example
    13Aug, 2022
    20 Examples of for loop and while loop in python tutorial

    Loops in python

    Loops are used in computer programming to repeatedly run a block of code.
    A loop can be used, for instance, to repeat a message 100 times. It’s only a simple illustration; loops may be used to accomplish much more.
    Python has two different kinds of loops:

    • while loop
    • for loop

     

    For loop in Python

    A for loop in Python is used to iterate over a sequence (such as a list, tuple, or string) or other iterable object. The general syntax of a for loop is:

     

    Basic for loop:

    A range is a collection of numbers that fall within two numerical intervals.

    for i in range(5):
    print(i)
    output
    0
    1
    2
    3
    4

    2. For loop with a list:

    fruits = [‘apple’, ‘banana’, ‘mango’]
    for fruit in fruits:
    print(fruit)
    output:
    apple
    banana
    mango

    3, For loop with a string:

    for char in “hello”:
    print(char)
    Output:
    h
    e
    l
    l
    o

    4.For loop with a tuple:

    numbers = (1, 2, 3)
    for number in numbers:
    print(number)
    Output:
    1
    2
    3

    5.For loop with a dictionary:

    ages = {‘Alice’: 22, ‘Bob’: 27, ‘Eve’: 19}
    for name, age in ages.items():
    print(f'{name} is {age} years old’)
    Output:
    Alice is 22 years old
    Bob is 27 years old
    Eve is 19 years old

    6.For loop with a nested list:

    matrix = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]
    for row in matrix:
    for col in row:
    print(col)
    output
    1
    2
    3
    4
    5
    6
    7
    8
    9

    7. For loop with a break statement:

    for i in range(5):
    if i == 3:
    break
    print(i)

    output:
    0
    1
    2

    8, For loop with a continue statement:

    for i in range(5):
    if i == 3:
    continue
    print(i)

    output
    0
    1
    2
    4

    9.For loop with a range and step:

    for i in range(10, 0, -2):
    print(i)

    output
    10
    8
    6
    4
    2

    10. For loop with an else clause:

    numbers = [1, 3, 5, 7]
    for num in numbers:
    if num % 2 == 0:
    print(f'{num} is even’)
    break
    else:
    print(‘No even numbers found’)

    11. For loop with enumerate and a list:

    fruits = [‘apple’, ‘banana’, ‘mango’]
    for index, fruit in enumerate(fruits):
    print(f'{index}: {fruit}’)

    output

    0: apple
    1: banana
    2: mango

    12, For loop with multiple iterators:

    numbers = [1, 2, 3]
    colors = [‘red’, ‘green’, ‘blue’]
    for number, color in zip(numbers, colors):
    print(f'{number} is {color}’)

    Output:
    1 is red
    2 is green
    3 is blue

    13. For loop that iterates over a reversed list:

    fruits = [‘apple’, ‘banana’, ‘mango’]
    for fruit in reversed(fruits):
    print(fruit)

    output:

    mango
    banana
    apple

    14. For loop that iterates over a sorted list:

    fruits = [‘apple’, ‘banana’, ‘mango’]
    for fruit in sorted(fruits):
    print(fruit)
    output
    apple
    banana
    mango

    15. For loop that iterates over a list in chunks:

    numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    for i in range(0, len(numbers), 3):
    chunk = numbers[i:i+3]
    print(chunk)
    output
    [1, 2, 3]
    [4, 5, 6]
    [7, 8, 9]
    [10]

    16.For loop that iterates over a list and removes elements:

    numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    for i, number in enumerate(numbers):
    if number % 2 == 0:
    numbers.pop(i)
    print(numbers)
    output
    [1, 3, 5, 7, 9]

    17. For loop that iterates over a list and replaces elements:

    numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    for i, number in enumerate(numbers):
    if number % 2 == 0:
    numbers[i] = ‘even’
    else:
    numbers[i] = ‘odd’
    print(numbers)
    output
    [‘odd’, ‘even’, ‘odd’, ‘even’, ‘odd’, ‘even’, ‘odd’, ‘even’, ‘odd’, ‘even’]
    18. For loop that iterates over a list and inserts elements:
    numbers = [1, 2, 3, 4, 5]
    for i, number in enumerate(numbers):
    numbers.insert(i+1, number+5)
    print(numbers)
    output

     

    Python While loop

    A block of code is regarded to consist of all the statements that are followed by the same number of character spaces following a programming construct. The way that Python groups statements is by indentation. When a while loop is run, expr is first tested in a boolean context, and if it returns true, the body of the loop is run. The expression is then verified again, and if it is still true, the body is run once again. This process is repeated until the expression is no longer true.

    •While loop execute a set a statement as long as test statement is true.
    •A relevant variable has to be declared at the beginning of the loop

     

    while loop flow chart python

    While loop falls under the category of indefinite iteration. Indefinite iteration means that the number of times the loop is executed isn’t specified explicitly in advance.Statements represent all the statements indented by the same number of character spaces after a programming construct are considered to be part of a single block of code. Python uses indentation as its method of grouping statements

     

    Example of While Loop

    Python While Loop

    # Python program to illustrate
    # while loop
    num = 0
    while (num < 3):
    num = num + 1
    print(“vista academy”)

    Output

    vista academy
    vista academy
    vista academy

    Single statement while block

    count = 0
    while (count < 5): count += 1; print(“Vista Academy”)

    Output
    Vista Academy

    Vista Academy

    Vista Academy

    Vista Academy

    Vista Academy

     

    Example of using the break statement in while loops

    To terminate a while loop prematurely in Python, use the break command.

    n = 1
    while n < 5:
    print(“Hello”)
    n = n+1
    if n == 3:
    break

    output
    Hello
    Hello

     

    Example of using the continue statement in while loops

    n = 1

    while n < 5:
    if n == 2:
    n = n+1
    continue
    print("Hello")
    n = n+1

    Output
    Hello
    Hello
    Hello

    The continue statement in Python may be used to end the current iteration of the while loop and begin the next one.

     

    Using if-elif-else statements inside while loop

    z = 0

    while z < 3:
    if z == 0:
    print(“z is”,z)
    z += 1
    elif z == 1:
    print(“z is”,z)
    z += 1
    else:
    print(“z is”,z)
    z += 1

    output


    z is 0
    z is 1
    z is 3

    Adding elements to a list using while loop

    List = []
    i = 0

    while len(List) < 4 :
    List.append(i)
    i += 1
    print(List)

    output
    [0,1,2,3]

     

    Python while loop to print a number series

    n = 10

    while n <= 100:
    print(n ,end = “,”)
    n = n+10

    OUTPUT
    10.20.30.40.50.60.70.80.90,100

     

    Counting the number of vowels in a string


    def count_vowels(s):
    s = s.lower()
    vowels = ‘aeiou’
    count = 0
    i = 0
    while i < len(s):
    if s[i] in vowels:
    count += 1
    i += 1
    return count

    print(count_vowels(‘hello’)) # Output: 2
    print(count_vowels(‘aeiou’)) # Output: 5
    print(count_vowels(‘abcdefghijklmnopqrstuvwxyz’)) # Output: 5

     

    Thanks, I’m not interested