How to Become a Data Scientist in 2024: Complete Guide

 

What Does a Data Scientist Do? 

A Data Scientist extracts, analyzes, and interprets data to uncover valuable insights and inform strategic decisions. They collect and clean data from various sources, perform exploratory data analysis to identify patterns, and create predictive models using ML and statistical techniques.

Data Scientists also play a crucial role in feature engineering, model evaluation, and deploying models into production. Their work spans industries, aiding businesses in optimizing operations, improving products, and driving data-driven strategies for success. They are instrumental in transforming data into actionable knowledge that drives innovation and competitive advantage.

Data Analyst vs Data Scientist: What's the Difference?

Data Analysts focus on examining data to uncover meaningful insights and trends, often using tools like Excel and SQL. They use historical data to help businesses make informed decisions.

Data Scientists, on the other hand, have a broader skill set. They not only analyze data but also build complex predictive models and develop algorithms. They use Python and R to extract deep insights, make future predictions, and create data-driven products. They bridge the gap between data analysis and ML to solve complex problems and drive innovation.

Qualifications and Eligibility Required 

To become a data scientist, you will need to have strong analytical and mathematical skills. You should be able to understand and work with complex data sets. Additionally, you should be able to use statistical software packages and be familiar with programming languages such as Python or R. Data scientists also typically have a certification from an accredited program.

Read More: Switching to data science was one of the best decisions Ekta Saraogi took for her career. After a varied career in the IT field, our Data Scientist Master's Program offered her the variety she craved with a more stable environment for her career. Read all about Saraogi’s career from IT nomad to Data Science master in her Simplilearn Data Science Course Review.

How to Become a Data Scientist?

Data science is the area of study that involves extracting knowledge from all of the data gathered. There is a great demand for professionals who can turn data analysis into a competitive advantage for their organizations. In a career as a data scientist, you’ll create data-driven business solutions and analytics.

Step 1: Earn a Bachelor’s Degree

A great way to get started in Data Science is to get a bachelor’s degree in a relevant field such as data science, statistics, or computer science. It is one of the most common criteria companies look at for hiring data scientists. 

Step 2: Learn Relevant Programming Languages

While a Bachelor’s degree might give you a theoretical understanding of the subject, it is essential to brush up on relevant programming languages such as Python, R, SQL, and SAS. These are essential languages when it comes to working with large datasets.

Step 3: Learn Related Skills

In addition to different languages, a Data Scientist should also have knowledge of working with a few tools for Data Visualization, Machine Learning, and Big Data. When working with big datasets, it is crucial to know how to handle large datasets and clean, sort, and analyze them.

Step 4: Earn Certifications

Tool and skill-specific certifications are a great way to show your knowledge and expertise about your skills.  Here are a few great certifications to help you pave the path:

These two are the most popular tools used by Data Scientist experts and would be a perfect addition to start your career journey.

Step 5: Internships

Internships are a great way to get your foot in the door to companies hiring data scientists. Seek jobs that include keywords such as  data analyst, business intelligence analyst, statistician, or data engineer. Internships are also a great way to learn hands-on what exactly the job with entail.

Step 6: Data Science Entry-Level Jobs

Once your internship period is over, you can either join in the same company (if they are hiring), or you can start looking for entry-level positions for data scientists, data analysts, data engineers. From there you can gain experience and work up the ladder as you expand your knowledge and skills.



7 Skills to Become A Data Scientist

To become a data scientist, you’ll need to master skills in the following areas:

  • Gain database knowledge which is required to store and analyze data using tools such as Oracle® Database, MySQL®, Microsoft® SQL Server and Teradata®.
  • Learn statistics, probability and mathematical analysis. Statistics is the science concerned with developing and studying methods for collecting, analyzing, interpreting and presenting empirical data. Probability is the measure of the likelihood that an event will occur.
    Mathematical analysis is the branch of mathematics dealing with limits and related theories, such as differentiation, integration, measure, infinite series, and analytic functions.
  • Master at least one programming language. Programming tools such as R, Python, and SAS are very important when performing analytics in data.
    R is a free software environment for statistical computing and graphics, which supports most Machine Learning algorithms for Data Analytics such as regression, association, and clustering.
    Python is an open-source general-purpose programming language. Python libraries like NumPy and SciPy are used in Data Science.
    SAS can mine, alter, manage and retrieve data from a variety of sources as well as perform statistical analysis on the data.
  • Learn Data Wrangling which involves cleaning, manipulating, and organizing data. Popular tools for data wrangling include R, Python, Flume, and Scoop.
  • Master the concepts of Machine Learning. Providing systems with the ability to automatically learn and improve from experience without being explicitly programmed to. Machine Learning can be achieved through various algorithms such as Regressions, Naive Bayes, SVM, K Means Clustering, KNN, and Decision Tree algorithms to name a few.
  • Having a working knowledge of Big Data tools such as Apache Spark, Hadoop, Talend, and Tableau, which are used to deal with large and complex data which can’t be dealt with using traditional data processing software.
  • Develop the ability to visualize results. Data visualization integrating different data sets and creating a visual display of the results using diagrams, chart, and graphs
Location: Tamil Nadu, India

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