There are numerous paths to becoming a data scientist, but as it is typically a high-level employment, data scientists have typically been well educated, having degrees in fields like computer science, mathematics, and statistics. But things are starting to shift.

Develop the Correct Data Skills

You can still become a data scientist if you lack relevant work experience, but you will need to build the necessary foundation in order to pursue a career in data science.

Data Scientist is a high-level career, thus before you specialise to that extent, you should have a solid foundation of expertise in a related area. This could be in the fields of mathematics, engineering, statistics, data analysis, programming, or information technology; some data scientists have even come from backgrounds in business and baseball scouting.

If Data Science is like a language, statistics is the grammar. Statistics is the process of studying and interpreting huge data sets. Statistics are as important to us as air when it comes to data processing and gathering insights. We can use statistics to decipher the hidden details in massive datasets.

LEARN STATISTICS

But whatever field you begin with, it should include the fundamentals: Python, SQL, and Excel. These skills will be essential to working with and organizing raw data. It doesn’t hurt to be familiar with Tableau as well, a tool you’ll use often to create visualizations.

Keep an eye out for opportunities to help you start thinking like a Data Scientist; the more this background lets you work with data, the more it will help you with the next step.

But no matter what area you start in, you should know Python, SQL, and Excel. These abilities will be necessary for handling and arranging raw data. Additionally, since you’ll use Tableau frequently to build visuals, it doesn’t hurt to be familiar with it.

The more your experience allows you to deal with data, the more it will aid you in the following phase, so keep an eye out for possibilities to help you begin thinking like a data scientist.

Learn the fundamentals of data science

A data science course or bootcamp can be an ideal way to acquire or build on data science fundamentals. Expect to learn essentials like how to collect and store data, analyze and model data, and visualize and present data using every tool in the data science toolkit, including specialized applications like visualization programs Tableau and PowerBI—among others.

By the end of your training, you should be able to use Python and R to build models that analyze behavior and predict unknowns, and be able to repackage data into user-friendly forms.

Many job postings list advanced degrees as a requirement for Data Science positions. Sometimes, that’s non-negotiable, but as demand outstrips supply the proof is increasingly in the pudding. That is, evidence of the requisite skills often outweighs mere credentialism.

What’s most important to hiring managers is an ability to demonstrate mastery of the subject in some way, and it’s increasingly understood that this demonstration doesn’t have to follow traditional channels.

Data Collection

In the discipline of Data Science, this is one of the most crucial tasks. This expertise necessitates familiarity with a variety of tools for importing data from both local systems as CSV files and scraping data from websites using the lovely soup python module. Scraping can also be done using an API. Knowledge of Query Language or Python ETL pipelines can help with data collection.

DATA CLEANING

As a Data Scientist, you’ll spend the majority of your time on this step. Data cleaning is the process of removing undesired variables, missing values, category values, outliers, and incorrectly reported records from raw data so that it can be used for work and analysis. Data cleaning is critical since real-world data is dirty, and attaining it with the help of numerous Python modules (such as Pandas and NumPy) is crucial for aspiring Data Scientists.

Acquaintance With EDA( Exploratory Data Analysis)

In the enormous subject of data science, EDA (exploratory data analysis) is the most significant part. It entails examining a variety of data, variables, data patterns, and trends, as well as extracting relevant insights from them using a variety of graphical and statistical tools. EDA detects a variety of patterns that a machine learning programme could miss. All data manipulation, analysis, and visualisation are included.

Study the essential programming languages for data science.

In order to clean, analyse, and model data, data scientists use a variety of specialised tools and software. Data scientists also need to be proficient in query languages like SQL and statistical programming languages like Python, R, or Hive in addition to general-purpose Excel.

RStudio Server, which enables a development environment for working with R on a server, is one of a Data Scientist’s most crucial tools. Another well-known programme that offers statistical modelling, data visualisation, machine learning capabilities, and more is open-source Jupyter Notebook.

Learn Python

Learning a computer language should be the first and most important step toward Data Science ( i.e. Python). Because of its simplicity, versatility, and pre-installation of strong libraries (such as NumPy, SciPy, and Pandas) essential in data analysis and other parts of Data Science, Python is the most frequent scripting language used by the majority of Data Scientists. Python is a free and open-source programming language that comes with a number of libraries.

Participate in data science projects to improve your real-world data skills.

You can start using the programming languages and digital tools that data scientists use after learning the fundamentals of them. This will allow you to put your newfound knowledge into practise and further develop your skills. Try to take on projects that need a variety of abilities, such as utilising Python and R to analyse data statistically, Excel and SQL to manage and query databases, and building models that study behaviour and produce fresh insights. You can also use statistical analysis to anticipate unknowns.

Try to touch on each stage of the process as you practise, starting with the preliminary analysis of a business or market area, followed by the identification and gathering of the appropriate data for the task at hand, cleaning and testing of that data to maximise its utility.

Develop visualisations and get presentation practise

Practice creating your own custom visualisations from start using tools like Tableau, PowerBI, or Infogram to determine the best method to let the data speak for itself.

Although the fundamental idea behind spreadsheets is simple—creating computations or graphs by correlating the data in their cells—Excel continues to be tremendously helpful after more than 30 years and is essentially indispensable in the field of data science.

But producing attractive visualisations is just the start. You must be able to utilise these visualisations to convey your findings to a live audience in your capacity as a data scientist. You might already have these communication abilities, but even if not, everyone can get better with practise. Before moving on to a group environment, start small, if required, by giving presentations to a single buddy or even your pet.

Create a portfolio to highlight your data science abilities.

Your next step is to exhibit these skills by creating the polished portfolio that will land you your ideal job. This is something you should accomplish after doing your preliminary study, receiving the necessary training, and practising your new skills by creating a wide range of impressive projects.

In fact, your portfolio can be the key factor in your success in finding a job. For instance, the Data Science Bootcamp at BrainStation is made to provide a project-based learning environment that aids students in developing a strong portfolio of successfully completed real-world projects. It is one of the best strategies for making an impression on employers.

Internships

The best approach to gain access to businesses seeking data scientists is through internships. Look for positions that mention terms like “data analyst,” “business intelligence analyst,” “statistician,” or “data engineer.” Internships are also a fantastic method to discover firsthand what a professional will actually involve.

Obtain Certifications

Certifications that are specialized to a tool or talent are an excellent method to demonstrate your knowledge of those skills. Here are several excellent certifications to aid you on your way: