Can a Fresher Become a Data Analyst? Entry Level Data Analyst Jobs |
    23Feb, 2022
    Can a fresher become a data analyst entry level data analyst jobs

    Data, data everywhere, but we have no idea which is valuable. Organizations require qualified professionals—data analysts—to extract insights from this massive data. A data analyst’s job is to process various data about the company’s customers, products, or goals and make it suitable for decision-makers.

    The data analysts’ data enables businesses to customise their products for customers, define marketing strategy, and make significant improvements to the manufacturing process. You’ll always have to deal with data, whether you’re looking for entry-level or senior-level data analyst jobs. This article will discuss how to get entry-level data analyst jobs and whether a fresher can become a data analyst. Let’s get started!


    What is Data Analytics ?

    Data analytics transforms raw data into useful insights. It consists of a variety of methods, technologies, and tools for applying data to identify patterns and address issues. Data analytics can influence business procedures, enhance decision-making, and promote company expansion.

    The average yearly income for a data analyst in India is 4.1 Lakhs, with salaries ranging from 1.6 Lakhs to 11.6 Lakhs. Salary projections are based on 68.1k in most recent pay for Data Analysts.

    Jobs for Entry-Level Data Analysts

    If you have a good GPA and have graduated from a data analysis programme, you can easily land entry level data analyst jobs. Someone with a degree in Mathematics, Statistics, or Economics who does not have a specialisation in data analysis can also get entry-level data analyst jobs.

    Most entry-level data analyst positions require at least a bachelor’s degree. A master’s degree in data analytics, on the other hand, is advantageous. Most people with technical backgrounds begin their careers as entry-level positions such as statistical assistant, business support analyst, operations analyst, or others, where they gain valuable on-the-job training and experience. But, what skills can help you land an entry-level data analyst position? Let’s look at the skills needed for entry-level data analyst positions.

    Obtain a basic education.

    If you’re new to the topic of data analysis, you should begin by learning the basics of the subject. You can assess whether this career is a suitable fit for you by gaining a wide understanding of data analytics and developing job-ready abilities.

    The majority of entry-level jobs for data analysts in the past required a bachelor’s degree. While many positions still do require a degree, that’s beginning to change. Although a degree in math, computer science, or a related subject might help you build basic knowledge and improve your career, you can also acquire what you need through the Vista Academy Bootcamp programme.

    Develop your technical abilities.

    Having a particular set of technical skills is often necessary to land a job in data analysis. These are some fundamental abilities you’ll probably need to get hired, whether you’re learning through a degree programme, professional certificate, or on your own.

    • Statistics
    • Python 
    • SQL (Structured Query Language) (Structured Query Language)
    • visualisation of data
    • Cleaning and preparing data

    Examine a few job postings for positions you’re interested in applying for, and concentrate your study on the particular programming languages or visualisation tools specified as criteria.

    Basic Understanding of Excel and Power Bi

    To get into an entry-level data analyst job, you should be acquainted with Microsoft Excel. Categorizing data and calculating numbers are among the significant tasks of a data analyst. Hence, it is helpful if you are comfortable using Excel.
    Good Knowledge of Statistics: One should be well-versed in Statistics and distributions, which are crucial in data analysis. You need to have a good hold on inferential and descriptive statistics, binomials and tests, and experimental and statistical design for an entry-level data analyst job.

    A Strong Hand in Mathematics:


    A data analyst communicates through numbers. As a result, you must be familiar with various types of mathematical operations. The entry-level position entails converting word problems into mathematical expressions. You should also be proficient in solving algebraic expressions, multivariable calculus, and various types of functions.

    Familiarity with Machine Learning:

     The foundation of machine learning is linear algebra and multivariable calculus, which allows data professionals to make accurate predictions. You should familiarise yourself with the three major types of machine learning: supervised, unsupervised, and reinforcement learning.

    Knowledge of programming languages:

    In the field of data analytics, programming skills are essential. You should be able to interpret data using programming languages as a data analyst. For an entry-level data analyst position, you should be proficient in at least one programming language. The more languages you know, however, the better. SQL, Python, R, C++, Java, MATLAB, and PHP are some of the programming languages used in data analysis.

    Work on projects with real data.

    Working with data in practical contexts is the best approach to discover its worth. 
    Keep an eye out for degree programmes or courses that feature practical projects using actual data sets. 

    You can also find a variety of free public data sets you can use to design your own projects.

    Top 5 Jobs for Entry-Level Data Analysts

    Several employers are currently hiring professionals for data analyst jobs all over the world. The top 5 entry-level data analyst jobs are listed below.

    Operations Analyst

    Operations analysts are typically employed by large corporations. An operations analyst’s responsibilities include working on in-house reporting systems, managing product manufacturing and distribution, and overseeing the overall restructuring of business operations. Operations analysts can work in a variety of industries, and their pay can vary greatly. The average salary for an entry-level operations analyst in India is 2,50,239.

    A quantitative analyst's job

    A quantitative analyst’s job is to study market statistics in order to develop analytical software and quantitative models. Quantitative analysts collaborate with Mathematical professionals to improve trading protocols and increase business profit. In India, the median salary for an entry-level quantitative analyst is Rs. 7,50,000.

    IT Systems Analyst:

    IT Systems Analyst: An IT systems analyst’s responsibilities include designing systems to solve IT-related problems. Some system analysts use third-party tools to test software, whereas others create new tools based on their knowledge of data analytics. In India, the average salary for an entry-level IT systems analyst is $2,81,354.

    Healthcare Data Analyst:

     Healthcare data analysts assist doctors and scientists in solving problems that they face on a daily basis. The healthcare industry generates massive amounts of data, and as regulatory standards for data storage and processing become more stringent, the demand for skilled healthcare data analysts is increasing.

    Data Analytics Consultant:

     Healthcare data analysts assist doctors and scientists in solving problems that they face on a daily basis. The healthcare industry generates massive amounts of data, and as regulatory standards for data storage and processing become more stringent, the demand for skilled healthcare data analysts is increasing.

    If an entry-level data analyst is familiar with all of the above-mentioned skills, the average monthly salary in India is 13,417. According to Analytics India Magazine, at their entry-level, 76 percent of analytics professionals earn between 0 and 6 lakhs. However, given the increase in the number of freshers in the data analysis domain in India, data analysts are in high demand. While the number of freshmen aspiring to be data analysts is increasing, they are finding it difficult to begin their careers from scratch. Let’s take a look at how new data analysts can get started.

    Can a New Graduate Work as a Data Analyst?

    It is difficult for a newcomer to land his or her first job in any field. When it comes to data analytics, many freshers today want to know, “Can a fresher become a data analyst?” Yes, the answer is yes. A newcomer can become a data analyst if he or she learns the tricks of the trade and works hard to develop the necessary skills. To get started, freshmen must plan how they will stand out in the field and keep up with those who already have relevant experience in the field.

    As a newcomer, you should consider the following factors to kickstart your data analyst career:

    Around 47 per cent of companies surveyed across the country intended to hire freshers. This is up from the global average of only 7 per cent, Economic Times reported quoting TeamLease EdTech.

    How to Land Your First Data analytics Job

    Getting your first job in data analytics is a difficult task. Many people come to me for advice on how to get their first job in data science. Many IT jobs have trainee positions where one can get on-the-job training. Data analytics is most emphatically not one of them. Data science teams are typically small and work on a variety of business problems. Data scientists are frequently expected to be self-sufficient from the start.
    I’m going to give you some pointers to help you land your first data science job. I’m not going to sugarcoat it by saying it’s easy. It will not be an easy journey, but following these guidelines will increase your chances of success

    Data analytics skills

    Analytical Skills


    To analyze massive datasets, spot trends, and extract valuable insights, data analysts need excellent analytical skills. This involves having the ability to use methods of statistics, make use of data visualization techniques, and exercise analytical thinking while solving challenging issues.


    Data Manipulation:


    A data analyst must be proficient in data manipulation. This involves carrying out data wrangling activities, integrating datasets, cleaning and preparing raw data, and transforming data into a suitable format. For data manipulation, an understanding of programming languages like SQL, Python, or R can be useful.


    Statistical Analysis


    A data analyst must have an in-depth knowledge of statistical theories and techniques. This covers regression analysis, hypothesis testing, descriptive statistics, and other statistical methods. It can be useful to be proficient in statistical software programmes like SPSS, SAS, or Excel.


    Data Visualization


    A key skill is an ability to use visualisations to effectively display data. When presenting data visually, data analysts should be skilled at using programs like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn.


    Domain Knowledge


    Your effectiveness as a data analyst can be considerably increased by having an understanding of the particular domain you’re working in. You can only ask relevant inquiries, come up with valuable feedback, and provide helpful suggestions if you are aware of the situation and the company’s goals.


    Problem-Solving


    Data analysts should be skilled at resolving problems. This requires the capacity to break down complex problems into simpler pieces, choose the proper data analysis methods, and obtain insights to solve the issue successfully.


    Communication Skills

    Data analysts must be able to explain their conclusions and insights to stakeholders of all technical and non-technical backgrounds. In order to explain the results of complicated data analysis in a way that is both clear and understandable, a person must possess strong written and vocal communication abilities.

     

    Technical abilities

    I’m not going to focus on developing the technical skills required of a data scientist here. To be eligible to apply for data science jobs, you must first complete the following steps.
    Getting to know a programming language

    Exploratory data analysis is something that you should learn about.

    Recognizing the various machine learning algorithms

    Capability to communicate effectively

    Creating a portfolio

     

    Creating a portfolio
    When you feel comfortable with the concepts of data science, it is time to advance. Concentrate on developing a portfolio to learn and showcase your data science work.
    When I say you should work on projects for your portfolio, I mean it. It does not imply undertaking a difficult project. As an example, consider a sophisticated and highly efficient recommendation system. It could be something as simple as your prediction model on the Titanic dataset or a housing price dataset.

    What can you expect from a data analyst job?

    During your training, what skills will you learn? To be honest, it depends on the company you work for, the business area you work in (e.g. sales, business operations, or product creation), and the type of analytics you use. You could, for example, focus on descriptive, prescriptive, or diagnostic analytics. Alternatively, you may study big data systems such as Apache Hadoop.

    Most data analyst internships will not be high-stakes, regardless of what you learn. You’ll still get to learn about important things, but in a secure setting. A good mentor will push you to your limits while without overburdening you. While your specific responsibilities will differ due to the reasons stated above, here are some typical activities to anticipate.

    Collaboration with various team members

    Data analytics courses (or self-study) on the internet can convey the impression that data analysts work alone. In actuality, they must work with a wide range of specialties to communicate and coordinate. Engineers, product designers, and managers are just a few of the types of people you’ll encounter. You’ll also use your talents in a variety of business fields, including sales, marketing, IT, and finance. An internship provides a true taste of data analytics variety. You’ll gain unique insights into how a genuine firm is conducted by supporting management.

    Data cleansing and database maintenance


    Have you completed an online course or practise projects? If that’s the case, you may have conducted data analytics using online datasets. However, these datasets are frequently pre-structured and cleaned. An internship will provide you with real-world data in all of its unstructured, messy beauty! You’ll receive hands-on experience cleaning data using Python and Excel. You could even learn how to employ big data structures to collect and store data. A organised education will teach you important theory, but you will need to apply what you’ve learned in the actual world.

    Performing data analysis


    Of course, the most important aspect of data analytics is conducting analyses. An internship allows you to put all of the statistical techniques you’ve learned into practise. You’ll need some theoretical knowledge of statistics as well as a variety of analytical approaches to get the job. However, once you’re in, this is your opportunity to shine. Finally, you’ll be able to put what you’ve learned in the classroom into practise.

     

    Visualizing data


    The goal of visualisation is to make data more understandable. An intern may be deemed unsuitable for a high-stakes analysis. However, for someone with little experience, making visualisations is a safer endeavour. You’ll probably get to experiment with the company’s internal data tools and apply your own techniques. These data visualisation tools are typically enterprise-level (i.e., paid) and will be unfamiliar to you. Microsoft Power BI, Tableau Desktop (which also has a free version), Salesforce Einstein Analytics, and SAP Analytics Cloud are some of the most popular.

    Entry level Data Analytics projects for beginners

    1. Projects that analyse exploratory data (EDA)

    Without exploratory data analysis, the stage where patterns or discoveries are made in the data, a data analyst’s task is left unfinished. It gives an overview of the general traits of data analysis and how to interpret it using data modelling approaches. Exploratory data analysis is the ideal method to uncover anomalies in numbers, which would have required lengthy, exhausting sessions to complete.

    The following subjects may be helpful when developing an EDA project:

    • Having an understanding of the material and developing an interesting and pertinent hypothesis
    • Using algorithms or data visualisation to solve problems
      Trends in Data Analysis
    • Recognising the connections between variables and interacting with graphs that represent data.

    2.Sentiment Evaluation 

    Understanding the positive or negative polarities of the viewers based on their sentiments can be the goal of a sentiment analysis project for data analysts. These extractions, which are based on opinions posted on websites and social media accounts, can help you understand the general stance your audience takes on a given concept. The numerous categories include joyful, furious, depressed, curious, etc.

    Social media monitoring and online reputation management for any brand

    • Particularly useful for monitoring and comprehending how consumers generally perceive your brand
    • Draw emphasis to important regions.
    • Anything new, like updates on influencer campaigns

    3. Creating chatbots

    Online firms must use chatbots because of their many functionalities. They can help automate customer service procedures and save time and money in the process. Powerful chatbots, which are laced with AI and machine learning algorithms, are everywhere we look, from wearable devices to automated messages in messaging apps.

    A chatbot is a clever computer software that mimics real user interaction through a chat interface. These bots respond to any spoken or written commands in this way and can follow a conversation. Being self-aware, they become smarter the more interactions they have.

    4. A system for recommending movies

    Building a reliable movie recommendation system, one of the simplest ways to provide user-customized services, might not be as simple as it looks. Since the idea is built on an abstract click approach, there would be numerous Machine Learning implementations. You would need significant access to vast data sets of users’ preferences, surfing history, and other information.

     

    Skills for entry-level data analyst

    1. Learn Tools of data analytics such as Excel, Powerbi, SQL, and Python.
    2. Taking some degree in Statistics and economics is required but not mandatory at all.
    3. Update your knowledge on the latest tools and trends in data analytics.
    4. Join some communities and groups to be updated with data analytics.
    5. Take certification from Vista Academy which is basically a classroom training.
    6. data analytics job is to storytelling, therefore, increasing your communication skills.
    7. Develop your analytics skills . The data analytics job is to find insight from data therefore they have to analyse data and take decisions.
    8. Join internship and freelance jobs to get some experience.
    data analyst job fresher
    Top points for Entry level data analyst resume for freshers in 2023
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    Data Analytics in the Banking and Finance Industry
    18Feb, 2022
    The Importance of Data Analytics in the Banking and Finance Industry

    The financial sector creates a tremendous amount of data. Did you know that big data in finance refers to petabytes of structured and unstructured data that helps banks and financial institutions predict client behaviour and develop strategies? The structured data that is maintained within an organisation enables crucial decision-making insights to be provided. Unstructured data provides substantial analytical options across many sources, resulting in higher volumes.

    Data analytics is becoming increasingly important in the banking and finance industry. With the rise of digital banking, financial institutions are now able to collect and analyze vast amounts of data to gain insights into customer behavior, identify trends, and make better decisions. Data analytics can help banks and other financial institutions to better understand their customers, identify potential risks, and develop more effective strategies for managing their finances.

    Data analytics can help banks and other financial institutions to better understand their customers and their needs.

    Every single day, the entire planet generates 2.5 quintillion bytes of data! Because of the vast amount of data we generate, most firms, including the banking and financial industry, are now looking for ways to leverage it to their advantage. But how are they going to accomplish it? Of course, big data is involved. To help you grasp it better, below are some of its many benefits in the context of banking.

    Currently, the banking industry has significant development possibilities thanks to big data analytics. Banks can better understand their customers’ demands and make more informed decisions thanks to big data analytics. They can thus react to market needs more quickly and effectively as a consequence. The level of service will almost certainly decline as more people use financial services. However, banks must exercise caution since they are in charge of protecting the money and personal data of their clients. The improper use of big data analytics might limit the development of your business. As a result, the bank will almost likely not be able to expand if it does not properly integrate Big Data Analytics.

    Large data Big Need

    Large data, big requirements
    Processing enormous amounts of data necessitates a significant amount of computing power. Banks must deploy strong servers that can run analytical software such as machine learning and artificial intelligence. Alternatively, they must invest in cloud-based software, however most financial institutions still choose on-premise database storage for security concerns.

    The financial services industry was one of the first to embrace big data analytics and apply it to strategic planning in order to spot market trends and achieve a competitive advantage. Predictive analytics enables speedier decision-making and long-term planning when deciding what products to give customers and when to sell them. When it comes to retail, AI, in particular, assists in driving this proactive strategy, preventing banking customer churn, and promoting best practises.

    Enhanced Decision Making:

    Big data provides banks and financial institutions with a wealth of information that can be analyzed to gain valuable insights. By leveraging advanced analytics techniques on large datasets, organizations can make data-driven decisions regarding risk management, customer segmentation, product development, and investment strategies.

     

    Improved Customer Insights:

    Big data enables banks to gain a deeper understanding of their customers. By analyzing customer data from multiple sources, including transactional records, online behavior, and demographic information, banks can personalize their offerings, tailor marketing campaigns, and improve customer experiences.

     

    Fraud Detection and Prevention:

    Big data analytics helps in identifying patterns and anomalies that may indicate fraudulent activities. By analyzing large volumes of transactional data in real-time, banks can quickly detect and prevent fraudulent transactions, minimizing financial losses and protecting customer assets.

     

    Risk Management:

    Big data analytics allows banks to better assess and manage risks. By integrating diverse data sources, including market data, customer data, and external data feeds, banks can enhance their risk models and make more accurate predictions. This helps in identifying potential risks and taking proactive measures to mitigate them.

     

    Regulatory Compliance:

    Big data plays a crucial role in regulatory compliance for banks and financial institutions. By analyzing vast amounts of data, organizations can ensure adherence to regulatory requirements, such as Anti-Money Laundering (AML), Know Your Customer (KYC), and Basel III guidelines. Big data analytics can aid in detecting suspicious activities, generating accurate reports, and maintaining compliance.

     

    Real-Time Analytics:

    Big data technologies enable real-time or near-real-time analytics, allowing banks to make faster decisions and respond promptly to market changes. This is particularly valuable in areas such as algorithmic trading, fraud detection, and customer service, where timely insights can lead to a competitive advantage.

     

    Operational Efficiency:

    Big data analytics can optimize operational processes in banking and finance. By analyzing data on transaction volumes, customer interactions, and resource allocation, organizations can identify inefficiencies, streamline operations, and reduce costs.

     

    Innovation and Product Development:

    Big data provides valuable insights for innovation and product development in the banking and finance industry. By analyzing market trends, customer feedback, and emerging technologies, organizations can identify new opportunities, develop innovative products and services, and stay ahead of the competition.

     

    In summary, the banking and finance industry needs big data analytics to unlock the potential of vast data volumes. By harnessing the power of big data, organizations can make more informed decisions, improve customer experiences, manage risks effectively, ensure regulatory compliance, drive operational efficiency, and foster innovation in a rapidly evolving industry.

    Customers' preferences should be monitored:

    Banks have access to a virtual goldmine of highly valuable data, much of it provided by consumers. As a result, financial institutions have a better grasp of what their customers desire, allowing them to give more relevant services, goods, and other offerings.

    Personalized Offerings:

    Banks can gain insights into their clients’ particular preferences, behaviours, and requirements by tracking their preferences. This helps organisations to provide personalised products and services that meet the individual demands of their clients, enhancing customer happiness and loyalty.

    Customized Marketing:

    Knowing your consumers’ preferences enables banks to create customized marketing initiatives. Organizations can generate customized marketing messages and send them through relevant channels by analyzing client data such as transaction history, browsing behavior, and demographic information. This promotes customer involvement and the effectiveness of marketing initiatives.

    Product Development:

    Tracking client preferences aids in discovering market gaps and opportunities. Banks can obtain insights into client pain areas by analyzing customer comments, complaints, and ideas and developing new products or improving existing ones accordingly. This enables organizations to remain competitive while meeting changing client demands.

    Enhancement of consumer Experience:

    Banks can leverage consumer preference data to deliver a seamless and personalized customer experience. Organizations may provide a personalized experience that suits individual preferences by understanding customers’ preferred communication channels, interaction preferences, and service expectations. This results in greater client satisfaction, loyalty, and favorable word-of-mouth referrals.

    Cross-Selling and Upselling:

    Banks can uncover cross-selling and upselling opportunities by tracking client preferences. Organizations can propose suitable items or services to customers based on their preferences and requirements by analyzing transactional data, customer behaviors, and historical patterns. This not only boosts client value but also improves the bank’s income generation.

    Retention and Churn Prevention:

    Monitoring customers’ preferences allows banks to proactively identify potential churn risks. By analyzing customer behavior, engagement levels, and satisfaction indicators, organizations can intervene with targeted retention strategies. By addressing customers’ needs and concerns, banks can improve customer retention rates and reduce customer churn.

    Competitive Advantage:

    Keeping an eye on customers’ preferences gives banks a competitive edge. By staying ahead of customer preferences, organizations can anticipate market trends, offer innovative solutions, and differentiate themselves from competitors. This helps in attracting new customers and retaining existing ones in a highly competitive industry.

    It’s important to note that monitoring customers’ preferences should be done with the utmost respect for privacy and data protection regulations. Banks should ensure that customer data is collected, stored, and analyzed securely and in compliance with applicable laws and regulations.

    In conclusion, monitoring and understanding customers’ preferences

    Improved user targeting

     It is obvious that big data can assist banks in better understanding their clients, among other things. Applying such insights to marketing efforts ensures that they are better focused and, as a result, poised to provide greater outcomes.

    Personalized marketing, which targets customers based on an analysis of their unique buying habits, also uses Big Data. Financial services organisations can use sentiment analysis to gather data from customers’ social media profiles in order to determine their demands and then construct a credit risk assessment. This can also aid in the creation of an automated, precise, and highly individualised customer service.

     By applying incentive optimization, attrition modelling, and compensation optimization, Big Data aids Human Resources management.

    Efficient Marketing Spend:

    Understanding customers’ preferences enables banks to allocate their marketing budgets more efficiently. By identifying the target audience based on preferences, demographics, and behaviors, organizations can focus their marketing efforts on the most relevant customer segments. This leads to better conversion rates, reduced marketing waste, and improved return on investment (ROI).

    Personalized Messaging:

    Improved user targeting allows banks to deliver personalized messages to their customers. By tailoring marketing communications based on customers’ preferences, interests, and needs, organizations can create more relevant and engaging content. Personalization fosters a deeper connection with customers and increases the likelihood of positive responses and conversions.

    Higher Conversion Rates:

    When marketing messages align with customers’ preferences and interests, the likelihood of conversion increases. By understanding customer preferences, banks can customize their offers, promotions, and recommendations to match individual needs. This targeted approach improves the chances of converting leads into customers and maximizing sales opportunities.

    Reduced Customer Churn:

    By targeting customers based on their preferences, banks can better meet their expectations and reduce the likelihood of churn. By delivering personalized experiences, relevant product recommendations, and timely offers, organizations can enhance customer satisfaction and loyalty. Satisfied customers are less likely to switch to competitors, resulting in improved customer retention rates.

    Cross-Selling and Upselling Opportunities:

    Improved user targeting helps banks identify cross-selling and upselling opportunities more effectively. By analyzing customers’ preferences and transactional data, organizations can identify additional products or services that align with customers’ needs. Targeted offers and recommendations increase the chances of successfully cross-selling or upselling to existing customers.

    Enhanced Customer Engagement:

    When banks deliver targeted and personalized experiences, customers are more likely to engage with the brand. By tailoring content, offers, and communications to match customers’ preferences, organizations can capture their attention and foster stronger relationships. Increased customer engagement leads to higher customer satisfaction, brand advocacy, and long-term loyalty.

    Competitive Advantage:

    Improved user targeting provides a competitive advantage in the banking and finance industry. By understanding customers’ preferences better than competitors, organizations can differentiate themselves in the market. Targeted marketing campaigns and personalized experiences give banks a unique position and attract customers who resonate with their offerings.

    To achieve improved user targeting, banks and financial institutions need to leverage advanced data analytics, customer segmentation techniques, and robust customer relationship management systems. It’s essential to ensure that customer data is collected and utilized in a responsible and compliant manner, respecting privacy regulations and maintaining data security.

    Overall, by leveraging customers’ preferences to enhance user targeting, banks can optimize their marketing strategies, increase conversions, reduce churn, and build stronger customer relationships, ultimately driving business growth and success.

    Customized services:

    Customized services: It is no secret that today’s clients are finicky and demanding. Now, in order to win them over and keep them loyal, banks are using big data to better understand their customers, their needs, and so on. This data is then utilised to personalise the company’s offers and services in order to improve sales and profits.

    Enhanced Customer Satisfaction:

    Customized services cater to the unique requirements of individual customers, resulting in higher levels of customer satisfaction. When customers receive personalized solutions that address their specific needs and preferences, they feel valued and understood. This fosters a positive customer experience and strengthens the relationship between the customer and the bank.

    Improved client Retention:

    Banks can boost client loyalty and retention by providing customised services. Customers are more likely to stay with the bank and use its services if they receive personalized attention and solutions that match their interests. Customers are less likely to switch to competitors when they receive personalised services.

    Banks can personalise their product offerings to specific consumers by offering customised services. By analysing their customers’ financial behaviours, transaction histories, and preferences, bankers can provide solutions that are tailored to their specific needs. This boosts the relevance and value of the products, resulting in increased adoption rates and happy customers.

    Personalized Financial Advice:

    Customised services enable banks to deliver personalised financial advice and counselling to customers. Understanding their clients’ financial goals, risk profiles, and preferences allows banks to provide personalised recommendations, investment strategies, and financial planning services. It enables consumers to make informed decisions and achieve their financial objectives.

    Tailored Communication and Marketing:

    Banks can deliver tailored communication and marketing campaigns thanks to customised services. Organisations can develop personalised communications and offers by segmenting clients based on their interests, demographics, and behaviours. This results in increased engagement, higher response rates, and more effective marketing outcomes.

    Customers benefit from the flexibility and convenience that customised services provide. Banks can tailor their services to clients’ chosen communication channels, interaction methods, and service delivery options by analysing their preferences. This offers a smooth and convenient banking experience that is personalised to individual tastes and needs.

    Competitive Differentiation:

    In the banking industry, customized services provide a competitive edge. When banks provide personalized experiences that address their client’s specific demands, they distinguish themselves from competitors who provide more generic services. In a highly competitive market, customization can be a crucial differentiation in attracting and retaining clients.

    Banks must use client data, advanced analytics, and customer relationship management technologies to efficiently deliver customized services. To protect consumer information and comply with applicable rules, it is critical to ensure that data privacy and security procedures are in place.

    Improved cybersecurity:

    Improved cybersecurity: Given the plethora of data security concerns and dangers that this industry faces on a daily basis, it’s no surprise that banks are looking to big data for assistance. To identify risky behaviour, mitigate risk, and so on, it usually entails the use of real-time machine learning and predictive analytics on big data.
    There is little doubt that the financial and banking sector’s digital revolution has had a tremendous impact on the world. Thankfully, with the exception of a few setbacks, the majority of these improvements have benefited customers first and companies second.

    WHY DATA ANALYSIS AND MACHINE LEARNING IN BANKING ?

    Sales and Marketing

    Marketing and sales
    In the banking business, analytics are now driving direct marketing and sales activities, demonstrating which initiatives will yield the biggest returns and how customer segmentation across categories may make cross-vertical marketing easier to handle.

    Campaigns customised to demographics’ specific wants and expectations are more likely to reach them. As a result of big data, the sales funnel has been changed by the power of analytics. Leads are now highly qualified and can be forwarded to the sales team, who can use additional procedures to decide which potential clients are most likely to become long-term customers.

    Lead Generation:

    Sales and marketing teams work together to generate leads, which are potential customers interested in the products or services offered by the bank or financial institution. This can be done through various channels such as advertising, content marketing, social media, referrals, and partnerships.

    Targeted Marketing Campaigns:

    Marketing efforts are directed towards specific customer segments or target markets. Banks analyze customer data, preferences, and behaviors to create personalized marketing campaigns that resonate with the target audience. These campaigns may include email marketing, digital advertising, direct mail, events, and other promotional activities.

    Product Positioning and Differentiation:

    Marketing helps banks position their products or services in the market and highlight their unique value proposition. Effective marketing strategies communicate the benefits, features, and competitive advantages of the bank’s offerings to attract and retain customers.

    Customer Relationship Management (CRM):

    CRM systems are used by sales teams to manage customer interactions, track leads, and track the sales pipeline. CRM systems provide a centralised store of client information, allowing sales teams to engage with prospects effectively, track interactions, and nurture relationships.

    Sales strategies and Relationship Building:

    To understand client needs, deliver customised solutions, and establish long-term connections, sales teams use numerous sales strategies such as consultative selling and relationship building. This includes active listening, effective communication, and making personalised recommendations depending on the needs of the customer.

    Cross-Selling and Upselling:

    Sales teams use existing customer relationships to uncover cross-selling and upselling opportunities. Sales professionals might propose extra products or services that correspond with the clients’ financial goals and needs by analysing their needs and financial profiles.
    Customer Retention Strategies: To implement customer retention strategies, both the sales and marketing departments collaborate. This includes delivering exceptional customer service, offering reward programmes, sending personalised communications, and engaging with customers on a continuous basis to enhance their loyalty to the bank or financial institution.

    Digital Marketing and Online Channels:

    As digital platforms gain popularity, banks invest in digital marketing techniques to reach and engage customers online. Optimising the bank’s website, utilising search engine marketing (SEM), social media marketing, content marketing, and leveraging digital advertising channels are all part of this strategy.

    Analytics and measurement:

    Key performance indicators (KPIs) like as customer acquisition rates, conversion rates, customer lifetime value, and return on marketing investment are used to monitor and measure sales and marketing operations. Analytics and data-driven insights aid in strategy refinement.

    Centers for data storage

    Centers for data storage
    Banks require enterprise-grade infrastructure and massive storage capacity to access the computational power required to evaluate large data and discover new patterns. A data centre can be costly, but it may be the most cost-effective solution to protect consumer privacy, financial data, and transactional data. To prevent unwanted access, security is paramount, and a zero-trust network is required. For smaller banks with limited resources, storing the most sensitive data on premises while storing the rest of the company’s data in the cloud may be recommended.

    Purchase patterns of customers

    Banks may use big data analytics to get the data they need to enhance services and satisfy customer needs. Based on their customers’ purchase patterns, banks can utilise transactional data to predict which customers can be sold which financial products. To keep ahead of the competition and to grow your consumer base, you must do this.

    Banks may organise and categorise their clients based on a range of factors with a better grasp of their transactional history. They’ll be able to produce customised marketing strategies that are directed at a certain demographic as a consequence. Additionally, banks may assess risks, decide whether a client wants benefits or investments, and decide whether to extend loans.

    Customer Segmentation:

    Analyzing purchase patterns helps in segmenting customers based on their buying behaviors. By identifying common patterns, such as frequency of purchases, purchase categories, and transaction amounts, banks can group customers into segments with similar characteristics. This segmentation enables targeted marketing, personalized offers, and tailored services for each customer segment.

    Product and Service Recommendations:

    Purchase patterns provide valuable information for recommending relevant products and services to customers. By analyzing past purchases, banks can suggest additional products or services that align with customers’ buying history and preferences. This helps in cross-selling and upselling, increasing customer engagement and maximizing revenue opportunities.

    Personalized Marketing Campaigns:

    Understanding purchase patterns allows banks to design personalized marketing campaigns. By leveraging data on customers’ past purchases, transactional history, and preferences, organizations can create targeted marketing messages and promotions. This increases the effectiveness of marketing efforts, improves customer response rates, and drives higher conversion rates.

    Fraud Detection:

    Analyzing purchase patterns helps in detecting fraudulent activities. By monitoring customer transactions and purchase patterns, banks can identify anomalies and unusual behaviors that may indicate fraudulent activities, such as unauthorized transactions or identity theft. This enables prompt intervention and fraud prevention measures, protecting both customers and the bank.

    Customer Retention:

    Purchase patterns provide insights into customers’ buying frequency and loyalty. By analyzing purchase histories, banks can identify customers who have reduced their purchasing activity or show signs of potential churn. With this knowledge, organizations can implement targeted retention strategies, such as personalized offers or loyalty programs, to retain valuable customers.

    Product Development and Innovation:

    Purchase patterns help banks identify emerging trends and customer preferences, informing product development and innovation strategies. By analyzing patterns across customer segments, organizations can identify new product opportunities, improve existing offerings, and stay ahead of competitors. This supports continuous innovation and ensures that banks deliver products and services that meet evolving customer needs.

    Financial Planning and Advice:

    Purchase patterns offer insights into customers’ financial behaviors and needs, enabling banks to provide relevant financial planning and advice. By understanding customers’ spending patterns, banks can offer personalized recommendations on budgeting, saving, and investment strategies. This strengthens the bank-customer relationship and positions the organization as a trusted financial advisor.

    To analyze purchase patterns effectively, banks need robust data analytics capabilities and systems that can process large volumes of transactional data. It’s essential to ensure compliance with data privacy and security regulations while handling customer transaction data.

    In conclusion, analyzing purchase patterns of customers allows banks to gain valuable insights into their preferences, behaviors, and needs. By leveraging this information, organizations can drive customer segmentation, personalize marketing efforts, detect fraud, enhance product recommendations, improve customer retention, and provide tailored financial advice. These insights contribute to better customer experiences, increased customer loyalty, and ultimately, the success of the bank.

    For investment banks, risk modelling

    The act of simulating the movement of a single asset, such as an interest rate, or a portfolio of assets (such as stocks, bonds, futures, options, etc.), in response to various scenarios is known as risk modelling. You may lower the overall risk of your portfolio and boost its performance when risk modelling is done appropriately and consistently across all assets.

    For instance, if a bank wishes to conduct an investment banking transaction, they must take the following into account:

    • What are the expected returns?
    • What dangers exist?
    • What is the likelihood of that?
    • How crucial is this transaction relative to other alternatives?

    Detection of fraud

    While reducing fraud is a typical objective for banks and other financial organisations, analytics may also be used to manage risk rather than only find fraud.

    Analytics can be used to categorise and rank specific consumers who are at risk of fraud before applying various levels of account monitoring and verification. Banks and other financial organizations might prioritize their efforts to detect fraud by looking at the risk of the accounts.

    Data Analytics and Machine Learning:

     Banks leverage data analytics and machine learning algorithms to analyze large volumes of transactional data and detect patterns indicative of fraudulent behavior. By analyzing historical transaction data, customer profiles, and other relevant information, algorithms can identify anomalies, unusual patterns, or suspicious activities that may indicate fraud.

    Transaction Monitoring: 

    Banks use real-time transaction monitoring systems to look for symptoms of fraud in incoming transactions. These systems evaluate transaction parameters such as transaction amount, location, frequency, and divergence from usual behaviour using established rules and algorithms. Unusual or suspicious transactions can set off alarms that require additional examination.

    Behavior Analysis: 

    Banks examine consumer behaviour patterns for aberrations that could suggest fraud. Banks can discover abnormalities or changes in behaviour that may be indicative of fraudulent activity by creating baseline behaviour for individual clients, including transactional patterns, spending habits, and geographical locations.


    Artificial Intelligence (AI) and Natural Language Processing (NLP): 

    Unstructured data sources like as emails, chat logs, and customer support interactions are analysed using AI and NLP technology. These technologies are capable of detecting suspicious language, keywords, or patterns that may indicate fraudulent activity or efforts.


    Network Analysis:

    Network analysis techniques are used by banks to uncover links between various companies involved in fraudulent activity, such as fraud rings or money laundering networks. Banks can find hidden connections and patterns indicative of fraudulent behaviour by mapping relationships and analysing transactional links.


    Enhanced Identity Verification: 

    Banks implement robust identity verification processes to ensure the authenticity of customers and prevent identity theft. This may involve multi-factor authentication, biometric verification, and identity document validation to ensure that individuals are who they claim to be.

    Collaboration and Information Sharing:

     Banks collaborate with other financial institutions, regulatory bodies, and law enforcement agencies to share information on fraud trends, techniques, and known fraudsters. This collaboration helps banks stay informed about emerging threats and improve their fraud detection capabilities.

    Continuous Monitoring and Adaptive Systems:

     Fraud detection systems are continuously updated and refined to keep up with evolving fraud techniques. Banks regularly review and update their fraud detection models, rules, and algorithms to adapt to new fraud patterns and emerging risks.

    It’s important to note that fraud detection efforts should be balanced with customer experience and privacy considerations. Banks must ensure that customer data is protected and that privacy regulations and legal requirements are adhered to throughout the fraud detection process.

    In summary, the detection of fraud in the banking and finance industry involves leveraging data analytics, machine learning, behavior analysis, AI, NLP, network analysis, identity verification, and collaboration. By employing these techniques, banks can proactively identify and mitigate fraudulent activities, protecting both themselves and their customers from financial harm.

    Credit risk assessment

    Analytics are used by banks and other financial institutions to control the risk of the loans they issue. This is achieved by keeping an eye on the customer-specific data they collect. These details might comprise, but are not restricted to:

    • A customer’s credit rating
    • use of credit cards (how much you owe)
    • the sums outstanding on several credit cards (total debt)
    • Amounts owed on various forms of credit (total credit vs. total debt)

    Analysis of historical data is used to determine a borrower’s creditworthiness or to determine the risk associated with approving a loan. The analysis’ findings aid banks and other financial organisations in assessing both their own and their clients’ risks.

    Creditworthiness Evaluation: Banks assess the creditworthiness of borrowers by analyzing their financial health, repayment capacity, and credit history. This involves reviewing financial statements, income documents, credit reports, and other relevant information to determine the borrower’s ability to repay the loan.

    Credit Scoring Models:

    Credit scoring models are used to quantify and standardize the assessment of credit risk. These models assign numerical scores to borrowers based on various factors such as credit history, income stability, employment status, debt-to-income ratio, and other relevant financial indicators. The scores help banks evaluate the level of risk associated with each borrower.

    Credit Analysis and Underwriting:

    Banks conduct thorough credit analysis and underwriting processes to assess the borrower’s credit risk. This includes evaluating the purpose of the loan, analyzing collateral (if applicable), assessing the borrower’s industry and market conditions, and conducting a comprehensive risk assessment.

    Financial Ratio Analysis:

    Financial ratio analysis is employed to assess the borrower’s financial performance and stability. Ratios such as debt-to-equity ratio, liquidity ratio, profitability indicators, and cash flow metrics are examined to evaluate the borrower’s financial health and ability to service debt obligations.

    Credit History and Payment Behavior:

    Banks review the borrower’s credit history, including their past borrowing and repayment patterns. This involves checking credit reports, credit scores, and delinquency records to assess the borrower’s payment behavior and identify any red flags or potential risks.

    Collateral Assessment:

    In cases where loans are secured by collateral, banks evaluate the quality and value of the collateral. This assessment helps determine the level of security the collateral provides in mitigating credit risk. Factors such as market value, marketability, and the existence of prior liens are considered in collateral valuation.

    Stress Testing and Scenario Analysis:

    Banks perform stress tests and scenario analysis to assess the borrower’s ability to withstand adverse economic conditions. By simulating various scenarios, such as economic downturns or interest rate fluctuations, banks evaluate the impact on the borrower’s repayment capacity and overall credit risk.

    Industry and Market Analysis:

    Banks consider the borrower’s industry and market conditions to assess credit risk. Factors such as industry trends, competition, regulatory environment, and market risks are analyzed to understand the borrower’s business environment and its potential impact on creditworthiness.

    Credit Rating Agencies:

    Banks may also consider external credit ratings provided by reputable credit rating agencies. These ratings provide an independent assessment of the borrower’s creditworthiness and can serve as an additional input in the credit risk assessment process.

    Ongoing Monitoring:

    Credit risk assessment is not a one-time process. Banks continuously monitor borrowers and their credit exposures throughout the loan lifecycle. This includes tracking changes in financial conditions, payment behaviors, and market dynamics to proactively manage credit risk and take appropriate actions when needed.

    Credit risk assessment is crucial for banks and financial institutions to make informed lending decisions, set appropriate terms and conditions, and manage credit exposures effectively. It helps mitigate the risk of default, maintain a healthy loan portfolio, and ensure the overall stability of the institution.

    FAQ

    Data analytics in the banking and finance industry refers to the process of examining large sets of financial data to uncover insights, patterns, and trends. It involves using various analytical techniques and tools to make data-driven decisions and improve business operations.

    Data analytics is crucial in banking and finance for several reasons:

    Risk Management: It helps identify and assess potential risks in lending, investments, and other financial activities, allowing institutions to make informed decisions and mitigate losses.

    Fraud Detection: Data analytics can detect unusual patterns and anomalies in transactions, aiding in the early detection and prevention of fraud.

    Customer Insights: By analyzing customer data, banks can understand customer behavior, preferences, and needs, enabling them to offer personalized services and products.

    Cost Reduction: Data analytics can optimize operational processes, reduce inefficiencies, and lower operational costs.

    Compliance: It assists in ensuring compliance with regulatory requirements by providing data for reporting and auditing purposes.

    Credit Scoring: Analytics models can assess creditworthiness more accurately, improving the quality of loan approvals and reducing default rates.

    Investment Decisions: Financial institutions can use analytics to assess investment opportunities and create well-informed investment strategies.

     

    Data Analysts’ Role and trends in 2023 and Beyond
    BEST DATA ANALYTICS COURSE
    13Feb, 2022
    How to Learn Python for data analytics (Step-By-Step) in 2023

    Here is a step-by-step guide to learning Python for data analytics in 2023:

    Here is a step-by-step guide to learning Python for data analytics in 2023:

    1. Start by learning the basic syntax and concepts of Python, such as variables, data types, loops, and control structures. Best to Join Vista Academy

    2. Next, familiarize yourself with the Python standard library, particularly the math, statistics, and date/time modules. These modules contain functions and methods that will be useful for data analytics tasks.

    3. After you have a solid foundation in Python, it’s time to start working with data. You’ll need to learn how to import, clean, and manipulate data using libraries like NumPy, Pandas, and Matplotlib.

    4. Once you’re comfortable with the basics of data manipulation, you can start learning more advanced techniques such as machine learning, data visualization, and data analysis. There are many online tutorials, courses, and resources available to help you learn these skills.

    5. As you learn, it’s important to practice your skills by working on real-world projects. This will help you apply what you’ve learned and give you a chance to demonstrate your knowledge to potential employers.

    6. Finally, consider getting certified in Python for data analytics. This can help you stand out in the job market and demonstrate your expertise to employer

     

    Introduction to Python for data Analytics

    Python is a high-level, interpreted programming language. It was first released in 1991 and has become one of the most popular programming languages in the world, with a large and active community of users and developers. Python is known for its simplicity, readability, and flexibility, as well as its support for object-oriented, imperative, functional, and procedural programming styles. Python is used for a wide range of applications, including web development, scientific computing, data analysis, machine learning, and artificial intelligence.

     

    The new oil is data. This statement demonstrates how data capture, storage, and analysis are at the heart of every modern IT system. It doesn’t matter if you’re making a business choice, forecasting the weather, investigating protein structures in biology, or creating a marketing strategy. All of these scenarios call for a multidisciplinary approach that includes the use of mathematical models, statistics, graphs, databases, and, of course, the commercial or scientific rationale that underpins the data analysis. As a result, we need a programming language that can handle all of these different data science requirements. Python stands out as one of these languages since it comes with a plethora of libraries and built-in capabilities that make it simple to meet the demands of data research.

     

    Python has libraries

    Python contains a number of libraries that contain a significant number of mathematical functions and analytical tools.

    The following libraries will be used in this course:

    Pandas – This library is used to do structured data operations such as importing CSV files, creating dataframes, and preparing data.

    Numpy is a library for mathematicians. There’s an N-dimensional array object, linear algebra, and the Fourier transform, among other things.

    Matplotlib is a library that allows you to visualise data.

    SciPy is a Python library that includes linear algebra modules.

    Comment in Python

    Comments are text blocks that live in your code but are disregarded by the Python interpreter when it runs it. You can use comments to describe the code so that you and other developers can understand what it does and why it’s written the way it is. Simply add a hash mark (#) before your remark text to make a comment in Python: Example : 
    # This is a comment on its own line

    Variable in python

    In Python, variables are names tied to a particular object. They keep a reference to the memory address where an item is stored, often known as a pointer. Once a variable has been assigned to an object, the object can be accessed by using the variable name. You must declare your variables advance. The syntax is as follows: 
    num = 100 #num is of type int
str = "Chaitanya" #str is of type string

    Identifiers - Variable name

    The term “identifier” refers to the name of a variable. When naming variables in Python, there are a few guidelines to follow. 1. The variable’s name must always begin with a letter or an underscore ( ). For example, the variables _str, str, num, and _num are all allowed names. 2. The variable’s name must not begin with a number. 9num, for instance, is not an acceptable variable name. 3. Variable names cannot contain special characters like percent, $, or #; they must only contain alphanumeric characters and underscore (A to Z, a to z, 0-9, or ). 4. In Python, variable names are case sensitive, therefore num and NUM are two separate variables.

    Python Variable Example

    num = 100
str = "VistaAcademy"
print(num)
print(str)
    practice in below 

    What is the meaning of a Python keyword?

    A reserved term in Python is one that you can’t use as the name of a variable, class, function, or anything else. These keywords have a specific meaning and are used in the Python programming language for specific purposes.

    Here is a list of the Python keywords. Enter any keyword to get more help.

    False            def           if                          raise
    None             del          import                   return
    True              elif          in                              try
    and              else          is                         while
    as                 except    lambda                 with
    assert           finally    nonlocal                  yield
    break           for            not
    class           from            or
    continue     global                                  pass

    Example of Variable

    Example of Python keyword
    num = 10
while num>5:
print(num)
num -= 1

    Python data Type

    1. Integer: Integers are whole numbers. They can be positive, negative, or zero. For example: 2, -5, 0.
    2. Float: Floats are numbers with a decimal point. They can be positive, negative, or zero. For example: 3.14, -2.5, 0.0.
    3. String: Strings are sequences of characters. They can be defined using single quotes (‘) or double quotes (“). For example: ‘hello’, “world”, “123”.
    4. Boolean: Booleans represent one of two values: True or False. For example: True, False.
    5. List: Lists are collections of items that are ordered and changeable. They can contain elements of any data type. Lists are defined using square brackets ([]). For example: [1, 2, 3], [‘apple’, ‘banana’, ‘cherry’], [True, False, True].
    6. Tuple: Tuples are similar to lists, but they are immutable (i.e., they cannot be changed). They are defined using parentheses (()). For example: (1, 2, 3), (‘apple’, ‘banana’, ‘cherry’), (True, False, True).
    7. Dictionary: Dictionaries are collections of items that are unordered and changeable. They are organized using key-value pairs. Dictionaries are defined using curly braces ({}). For example: {‘name’: ‘John’, ‘age’: 30}, {1: ‘apple’, 2: ‘banana’, 3: ‘cherry’}, {True: ‘yes’, False: ‘no’}.
    8. Set: Sets are collections of items that are unordered and unchangeable. Sets are defined using curly braces ({}). For example: {1, 2, 3}, {‘apple’, ‘banana’, ‘cherry’}, {True, False}.
    I hope this helps! Let me know if you have any questions.

    Numeric Types

    Integer –

    In Python 3, there is no upper bound on the integer number which means we can have the value as large as our system memory allow 
    x = 20

#display x:
print(x)

#display the data type of x:
print(type(x))

    Float

    It is automatically inferred based on the value we are assigning to a variable. For example here fnum is a float data type. 
    # float number
fnum = 34.45
print(fnum)
print("Data Type of variable fnum is", type(fnum))

    Complex Number

    x = 1j 
    #display x:
print(x)

#display the data type of x:
print(type(x))

    Python Data Type – String

    String is a sequence of characters in Python. The data type of String in Python is called “str”. Strings in Python are either enclosed with single quotes or double quotes. # Python program to print strings and type 
    s = "This is a String"
s2 = 'This is also a String'

# displaying string s and its type

print(s)
print(type(s))
    print(s2) print(type(s2))

    Boolean Values

    In programming you often need to know if an expression is True or False. You can evaluate any expression in Python, and get one of two answers, True or False. Example 1 
    print(10 > 9)

print(10 == 9)

print(10 < 9)
    Example 2
    
a = 200
b = 330

if b > a:
print("b is greater than a")
else:
print("b is not greater than a")

    Operator in Python

    + addition

    x = 5
y = 3

print(x + y)

    -Subtraction

    x = 5
y = 3

print(x - y)

    *Multiplication

    x = 6
y = 3

print(x * y)

    /Division

    x = 12
y = 7

print(x / y)

    %Modulus

    x = 5
y = 2

print(x % y)

    **Exponentiation

    x = 2
y = 5

#shows remender of division

print(x ** y) #same as 2*2*2*2*2

    //Floor division

    x = 15
y = 2

print(x // y)

#the floor division // rounds the result down to the nearest whole number

    Python Assignment Operators

    x +=

    x = 5

x += 3

print(x)
    #answer will be 8

    -=

    x = 5

x -= 3

print(x)
    #answer will be 2

    *=

    x = 5

x *= 3

print(x)
    #answer will be 15 

    /=

    x = 5

x /= 3

print(x)

    %=

    x = 5

x%=3

print(x)
    #shows remainder 2

    //

    x = 5

x//=3
print(x)
    #shows result in integer 2

    **=

    x = 5

x **= 3

print(x)
    #mutiply 5*5*5

    &=

    x = 5

x &= 3

print(x)

    Python Logical Operator

    and

    Logical AND: True if both the operands are true.

    or

    Logical OR: True if either of the operands is true x or y

    # Print a and b is False
    print(a and b)

    # Print a or b is True
    print(a or b)

    # Print not a is False
    print(not a)

     

    Data Structure in Python

    Data structures in Python are ways of organizing and storing data in a structured and efficient manner. They provide different ways to store, access, and manipulate data based on their properties and intended use cases. Python offers several built-in data structures, each with its own advantages and use cases. Some common data structures in Python are:

    Python Data Type – List

    List is similar to tuple in that it is an ordered collection of elements. However, unlike tuple, list is a changeable data type, which means it can be altered. 
    # list of integers
lis1 = [1, 2, 3, 4, 5]
# prints entire list
print(lis1)

    # list of strings
lis2 = ["Apple", "Orange", "Banana"]
# loop through list elements
for x in lis2:
print (x)
    # List of mixed type elements
lis3 = [20, "Chaitanya", 15, "BeginnersBook"]
'''
Print a specific element in list
indexes start with zero
'''
print("Element at index 3 is:",lis3[3])

    Python Data Type – Tuple

    In Python, a tuple is an unchanging data type, which means it cannot be modified. It’s a list of elements that are separated by commas and enclosed in round brackets.

    t1 = (1, 2, 3, 4, 5)
# prints entire tuple
print(t1)

    # tuple of strings

    t2 = ("hi", "hello", "bye")
# loop through tuple elements
for s in t2:
print (s)
    # tuple of mixed type elements
t3 = (2, "Lucy", 45, "Steve")
'''
Print a specific element
indexes start with zero
'''
print(t3[2])

    Python data type Dictionary

    In Python, a dictionary is a collection of keys and values. It is an unordered and changeable data type, and works like an associative array in other programming languages. Here is an example of how you can create a dictionary in Python: 
    my_dict = {'name': 'John', 'age': 30, 'city': 'New York'}
 print(my_dict)
{'name': 'John', 'age': 30, 'city': 'New York'}

 name = my_dict['name']
print(name)
    John You can also add, remove, and modify the elements in a dictionary: 
    my_dict['age'] = 25
print(my_dict)
{'name': 'John', 'age': 25, 'city': 'New York'}
    < 
    
 my_dict[‘phone’] = ‘555-555-5555’
print(my_dict)
    {'name': 'John', 'age': 25, 'city': 'New York', 'phone': '555-555-5555'}
    output 
    del my_dict['phone']
print(my_dict)
{'name': 'John', 'age': 25, 'city': 'New York'}

    Sets:

    • Sets are unordered collections of unique items.
    • They do not allow duplicate values.
    • Useful for checking membership and performing set operations (union, intersection, etc.).
    • Defined using curly braces: my_set = {1, 2, 3}
    Sets are useful when you want to store a collection of items without any duplicates and quickly check for membership or perform set operations like union, intersection, and difference. Sets are defined using curly braces {} or the set() constructor. Here’s an example of using sets in Python:
    Creating a Set:
    You can create a set using curly braces {} or the set() constructor. Sets automatically remove duplicate elements, so only unique elements are stored.   
    # Creating a set using curly braces
my_set = {1, 2, 3, 4, 4, 5}
print(my_set)  # Output: {1, 2, 3, 4, 5}

# Creating a set using the set() constructor
another_set = set([3, 4, 5, 6, 6])
print(another_set)  # Output: {3, 4, 5, 6}
    Adding Elements to a Set:
    my_set = {1, 2, 3, 4, 5}
my_set.add(6)
print(my_set)  # Output: {1, 2, 3, 4, 5, 6}

    Removing Elements from a Set:

    You can remove an element from a set using the remove() method. If the element doesn’t exist in the set, it will raise a KeyError
    my_set = {1, 2, 3, 4, 5}
my_set.remove(4)
print(my_set)  # Output: {1, 2, 3, 5}

    Checking Membership in a Set:

    my_set = {1, 2, 3, 4, 5}
print(3 in my_set)  # Output: True
print(6 in my_set)  # Output: False

    Set Operations: 

    set_a = {1, 2, 3, 4, 5}
set_b = {4, 5, 6, 7, 8}

# Union of two sets
union = set_a | set_b
print(union)  # Output: {1, 2, 3, 4, 5, 6, 7, 8}

# Intersection of two sets
intersection = set_a & set_b
print(intersection)  # Output: {4, 5}

# Difference between two sets
difference = set_a - set_b
print(difference)  # Output: {1, 2, 3}

# Symmetric difference (elements in either set, but not both)
symmetric_diff = set_a ^ set_b
print(symmetric_diff)  # Output: {1, 2, 3, 6, 7, 8}

    if statement in python

    In Python, an if statement is used to execute a block of code only if a certain condition is true. Here is the general form of an if statement in Python: if condition: # code to be executed Here is an example of an if statement in Python: 
    x = 10
if x > 5:
print("x is greater than 5")
    In this example, the condition is x > 5. If the condition is true (in this case, it is), then the code inside the if block will be executed. In this case, the code inside the if block is print(“x is greater than 5”), which will print the string “x is greater than 5” to the console. You can also include an else clause in an if statement to specify a block of code that should be executed if the condition is false: 
    x = 3
if x > 5:
print("x is greater than 5")
else:
print("x is not greater than 5")
    n this example, the condition is x > 5, which is false. Therefore, the code inside the else block will be executed, which will print the string “x is not greater than 5” to the console. You can also use the elif keyword to specify additional conditions that should be checked if the initial if condition is false. For example 
    x = 3
if x > 5:
print("x is greater than 5")
elif x == 5:
print("x is equal to 5")
else:
print("x is less than 5")
    In this example, the initial condition is x > 5, which is false. The second condition, x == 5, is also false. Therefore, the code inside the else block will be executed, which will print the string "x is less than 5" to the console.

    Loops in python

    Loops are used to execute a block of code repeatedly for a certain number of times or until a certain condition is met. There are two types of loops in Python: for loops and while loops. Here is an example of a for loop that iterates over a range of numbers and prints each one to the console: 
    for i in range(10):
print(i)
    This will output the numbers 0 through 9. Here is an example of a while loop that continues to execute as long as a certain condition is true: 
    x = 0
while x < 10:
print(x)
x += 1
    This will output the numbers 0 through 9, just like the for loop above. Both for loops and while loops can be controlled using the break and continue statements. The break statement will exit the loop completely, while the continue statement will skip the rest of the current iteration and move on to the next one. I hope this helps! Let me know if you have any questions.

    While Loop in python

    A while loop in Python allows you to repeat a block of code as long as a certain condition is True. Here is the general syntax: while condition: code block The code block will be executed repeatedly as long as the condition is True. When the condition becomes False, the loop will terminate and control will be passed to the next line of code after the loop. Here is an example of a simple while loop that counts down from 5 and prints the count at each iteration: 
    count = 5
while count > 0:
print(count)
count -= 1
    This will output the following: 5 4 3 2 1 It is important to include a way to modify the condition within the loop, or else the loop will run forever (this is known as an infinite loop). In the example above, the count variable is decremented by 1 each time the loop runs, so eventually count will become 0 and the condition will be False, terminating the loop. You can also use the break statement to terminate a while loop early, before the condition becomes False. 
    count = 5
while True:
print(count)
count -= 1
if count == 0:
break
      This loop will run the same as the previous example, but it uses an infinite loop and the break statement to achieve the same result.

    function in python

    Step 1: Defining a Function

    In Python, a function is a block of code that performs a specific task and can be reused throughout your program. To define a function, you use the def keyword followed by the function name and a pair of parentheses. Here’s an example:     
     >def greet(name):
print(f"Hello, {name}!")
    In this example, we’ve defined a function called greet that takes one parameter name.

    Step 2: Function Parameters

    Parameters are the inputs that a function receives. You can pass values (arguments) to a function when you call it. In the above example, name is a parameter.

    Step 3: Function Body

    The indented block of code under the def statement is the function body. This is where you write the code that performs the desired task. In our greet function, the body contains a single line that prints a greeting.

    Step 4: Calling a Function

    To use a function, you call it by using its name followed by parentheses. You can pass values (arguments) to the function if it expects any. Here’s how you call the greet function:
    greet("Alice")
    This will output: Hello, Alice!

    Step 5: Return Statement (Optional)

    Functions can also return values back to the caller using the return statement. Here’s an example:
    def add(a, b):
    return a + b
    In this example, the add function takes two parameters (a and b) and returns their sum.

    Step 6: Function Call and Return Value

    When you call a function that uses the return statement, you can capture the returned value in a variable:
    result = add(5, 3)
print(result)  # This will print 8

    Step 7: Default Parameters (Optional)

    You can provide default values for function parameters. These values will be used if the caller doesn’t provide a value for that parameter:
    def power(base, exponent=2):
    return base ** exponent

    example of function in python

    CALCULATOR WITH IF FUNCTION

    def add(x, y):
    return x + y

def subtract(x, y):
    return x – y

def multiply(x, y):
    return x * y

def divide(x, y):
    if y != 0:
        return x / y
    else:
        return “Cannot divide by zero”

print(“Select operation:”)
print(“1. Add”)
print(“2. Subtract”)
print(“3. Multiply”)
print(“4. Divide”)

choice = input(“Enter choice (1/2/3/4): “)

num1 = float(input(“Enter first number: “))
num2 = float(input(“Enter second number: “))

if choice == ‘1’:
    print(“Result:”, add(num1, num2))
elif choice == ‘2’:
    print(“Result:”, subtract(num1, num2))
elif choice == ‘3’:
    print(“Result:”, multiply(num1, num2))
elif choice == ‘4’:
    print(“Result:”, divide(num1, num2))
else:
    print(“Invalid Input”)

    To-Do List Program:

    tasks = []
def add_task(task):
tasks.append(task)
print("Task added:", task)

def remove_task(task):
if task in tasks:
tasks.remove(task)
print("Task removed:", task)
else:
print("Task not found")

def display_tasks():
print("Tasks:")
for index, task in enumerate(tasks, start=1):
print(f"{index}. {task}")

while True:
print("Select operation:")
print("1. Add Task")
print("2. Remove Task")
print("3. Display Tasks")
print("4. Quit")

choice = input("Enter choice (1/2/3/4): ")

if choice == '1':
task = input("Enter task: ")
add_task(task)
elif choice == '2':
task = input("Enter task to remove: ")
remove_task(task)
elif choice == '3':
display_tasks()
elif choice == '4':
break
else:
print("Invalid Input")
    Function with Default Argument:
    def power(x, exponent=2):
    return x ** exponent

result = power(5)  # Uses default exponent (2)
print(result)
result = power(3, 4)  # Custom exponent (4)
print(result)
    Top 10 reasons for Power BI is the finest tool for data analytics
    7Feb, 2022
    Top 10 reason for Power BI is the finest tool for data analytics

    What is Power BI?

    Power BI is a data-sharing platform that is hosted in the cloud. You can share your experiences with your colleagues once you’ve created reports with Power Query, Power Pivot, and Power View. This is where Power BI comes into play. You may upload Excel workbooks to the cloud and share them with a select set of coworkers using Power BI, which is actually a feature of SharePoint online. Not only that, but your colleagues can use filters and slicers to emphasise data in your reports. They’re finished by Power BI, a straightforward way to share your Microsoft cloud-based analysis and insights.

    You may use Power BI features to:

     

    Collaborate with your colleagues on presentations and questions.

    Update your Excel file using data from on-site sources.

    Power BI for Data Analysis

    Visualizations that are interactive and that are simple to integrate with Microsoft software


    Power BI adds tremendous value by providing a consolidated view of your company’s performance through stunning visualisations and interactive dashboards. Power Business Intelligence Services Desktop offers a free canvas for drag-and-drop navigation, a large library of interactive visualisations, and easy report creation and publishing in the Power BI service for the analyst at heart. You can also make unique, more complex visualisations with Power BI. Because Microsoft Power BI is part of the Microsoft business product suite, it has tight connectivity with other prominent Microsoft business products and tools including Microsoft Excel, Azure, and SQL Server.

    On powerbi.microsoft.com, you have access to a large library of educational materials.

    Microsoft offers a familiar and easy user interface that takes little time to get used to. Users get access to an extensive information base. New users can access tutorials, videos, presentations, and blogs to help them maximise the tool’s potential and solve problems quickly. Not to mention the Microsoft community, which has over a million members who share their experiences and seek solutions to their questions.

    Graphics analysis does not necessitate programming knowledge.

    Power BI can help analysts who perform graphics analyses on a regular basis. Simple data analysis, fast and dynamic calculations, and a wide range of visualisation options will help you save time when conducting ad-hoc analyses.

    Additional data sources


    Power BI can connect to more than 60 prominent solutions used by businesses today. Spark, Hadoop, and SAP are just a few examples. Data can be integrated directly with the Power BI engine, so users don’t have to model data in their source systems. The system also works with the most popular operating systems, including Windows, iOS, and Android.

    Power BI has excellent data modelling capabilities.

    Data modeling is one of the functions that is used to connect multiple data sources in a BI tool using relationships. The relationship defines how data sources are connected to each other and based on these data valuable visualizations can be created.

    With the data modeling function, you can create custom calculations on existing tables. The results of these calculations can be directly presented in Power BI visualizations.

    Reports and data analysis are securely shared.

    Users can publish their reports and visualisations immediately on the Internet and send them in e-mails using the Microsoft Power BI service. It can be accessed by anyone who is interested. Report access can be restricted to a single person, or a single report can be made available to everyone in the company, but the data displayed can be restricted to a specific group of people. For example, Boston-based sellers have access to solely Boston-based sales data, whereas the company’s CEO gets access to sales across the United States.

    Power BI integration is unrestricted. Embedding and the ability to embed in a third-party programme are both available.

    Microsoft Power BI’s inventors claim that their solution can be integrated into any software ecosystem. 
    Numerous links with major third-party software and Microsoft technologies demonstrate this. 

    As mentioned earlier, Microsoft is constantly working to extend the connectivity of this system, enabling users to import even more databases and data sources.

    Power BI Pro's data analysis gives you immediate access to reports and dashboards.

    You can share desktops and reports with the right people from anywhere in the world using Power BI. The system is also versatile because it works with popular operating systems like as Windows, iOS, and Android. Any updates or changes to the data will trigger alerts, allowing you to quickly analyse the most important KPIs and take strategic action.

    Unlimited access to local data (on-premises) and data in the cloud

    Power BI stores all local information and data in the cloud in one centralized location that can be accessed anytime, anywhere. You can also use pre-built links to improve data import from solutions such as Marketo, Salesforce, Google Analytics and many more. Additionally you are able to connect to different types of on-premises data storages such as SQL Server, Oracle or Hadoop. It also have pre build connectors to such cloud storages as Google BigQuery or all storages from Azure offering.

    different PowerBI views

    Report View: This is the default view that displays data visualisation in reports. You may use a variety of templates and visualisations to build several report pages.

    Data View: The converted data is shown in a table format with columns and rows. It also lets you construct new calculated columns for even more information.

    Model View, also known as Relationship View, aids in the creation of data model relationships. This view displays all of the models built in the data, allowing you to compare or build diagrams depending on subsets of the model.

    Power BI comes in a variety of formats.

    Power BI Desktop may be downloaded and installed on your personal computer, allowing you to connect to a data source, convert your data, analyse it, and visualise it using templates.

    Power BI services include: It is a SaaS (software as a service) cloud-based service (software as a service). You can also link to data here, although modelling options are limited.

    Power BI mobile app: The PowerBI app, which is available for iOs, Android, and even Windows, allows users to securely access dashboards and reports from any device.

    The Importance of Data Analytics in the Banking and Finance Industry
    DATA ANALYTICS COURSE

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