Guide to Learn Data Science and Become a Data Scientist

The internet is full of resources for learning about Data Science, but it is crucial to have a structured learning plan. The first step in this journey is to understand the basics of Data Science, including its definition and a typical day in the life of a data scientist. This article will outline the steps to become a professional data scientist by focusing on all the key skills required.

Let's take a closer look at job descriptions from top tech companies to see what they expect from a data scientist. This will help us understand the required skills and what it takes to secure a career in this field.

Job Descriptions for Data Scientist or Data Analyst roles

We will find three main sections in all the Job Descriptions:

  • Job Overview
  • Roles and Responsibilities
  • Required Skills

Let's understand each of these three fields in detail.

Job Overview

This section summarizes the overall requirement in one or two paragraphs. Sometimes, it also contains information about the project for which they are hiring and what is going to be their work culture. For example, companies providing the facility of remote work (work from home) can mention such benefits in the Job Overview section. A sample of the job overview is presented below:

As a Data Scientist in XYZ, you will be doing data mining, statistical analysis and scripting to extract relevant data through SQL. You will use the extracted data to find the trends, and relevant information. You will also apply various data analytics and ML techniques on wider domain of business problems linked with data. Some traits we are expecting in the candidate are:

  • A team player
  • A collaboration champion
  • Comfortable being uncomfortable
  • Open for feedbacks
  • A problem solver
  • Comfortable with multiple projects
  • Business and tech-curious

Roles and Responsibilities

This section lists all the relevant tasks for which that company is hiring. It is the most crucial section in any job description as we get a sense of our job and what tasks we will be doing if we get selected. If some tasks do not match our interests, we can pre-talk about them in the interview. A sample of the roles and responsibilities section is shown below:

  • Collecting and interpreting data
  • Defining new methods for data collection and analysis
  • Building machine learning models to predict user trends
  • Analyzing user behavior and continuously improving the model with new data
  • Presenting results with visually appealing techniques
  • Conducting thorough business hypothesis testing and verifying the hypothesis with data
  • Working with business analysts and data engineering teams to achieve goals.

Required Skills 

Every job, whether entry-level or experienced, demands a certain level of skillset. If the job is entry-level, employers demand our educational background or academic project experiences be inlined with their expectations. And for professional positions, they expect candidates to come with proven work experience. This section also mentions the qualifications/degrees we should have to apply for the particular position. A sample of the required skills section from a job description:

  • B.S./B.Tech/M.S./M.Tech degree in Applied Mathematics, Computer Science, Electrical Engineering or a related field.
  • Good experience in Python programming and familiarity with libraries such as Pandas, Numpy, and Scikit-learn.
  • Deep understanding of both supervised and unsupervised machine learning algorithms, including classification, clustering, and regression.
  • Good knowledge of Big Data processing using Hadoop and Spark.
  • Familiarity with Git, Flask, and REST APIs.
  • Understanding of A/B testing and hypothesis testing.
  • Solid understanding of the tools and techniques necessary for data analysis and pre-processing.

This summarizes any job description for the Data Scientist and Data Analyst role. The reason for explaining the JD for these roles is to make learners aware of what is required to become a Data Scientist. Now, we will cover the steps to becoming one by covering all the relevant skills.

Steps to become a Good Data Scientist

7 steps to learn data science and become a good data scientist

Step 1: Gaining proficiency in problem-solving using Python/R programming

Problem-solving skills are crucial in computer science. During job interviews, interviewers often ask candidates to solve coding exercises using a programming language. Python and R are the preferred languages in the machine learning and data science fields. To be proficient in these areas, it's recommended to know Python, which has a wider community and library support. 

Companies expect data scientists to know libraries like Pandas (for data reading and processing), Numpy (for mathematical operations on data), and Scikit-learn (for machine learning on data).

Step 2: Getting familiar with techniques like Hadoop and PySpark to handle Big Data

Big tech companies such as Meta (Facebook) and Google collect massive amounts of diverse data samples, totalling Petabytes daily. Traditional methods for processing numerical and tabular databases are insufficient for this task, leading to the emergence of Big Data and associated technologies like Hadoop and Pyspark. Companies in this space typically require a strong understanding of how Hadoop and Spark work.

Step 3: Getting familiar with APIs, Databases, and SQL for efficiently fetching the data

Data collection and generation is one of the biggest tasks that companies expect every Data scientist should know. Data Scientists use APIs to collect datasets from third-party websites and test their hypotheses. Once verified, they present their findings to the business stakeholders and help pivot business decisions. 

Once this dataset is fetched from third-party servers, it needs to be stored somewhere. There comes the need for a database. Traditional databases were MySQL and Oracle to store tabular format datasets. Non-tabular datasets need to be stored with modern databases like MongoDB and Cassandra. 

We use scripting languages based on these databases to fetch and analyze data. If the dataset is structured, the scripting language will be SQL; if the dataset is unstructured, it will be No-SQL.

Step 4: Gaining hands-on experience in Data Analysis and Visualization Techniques or Tools

Data Science is a field where technologies do not make you a Data Scientist, but your traits and desire will make you a good Data scientist. Three common and most required traits in any data scientist are: Curious, Judgemental, and Argumentative. The more curious we are about data, the more proficient we will be in data science. 

This curiosity is directly linked to Data Analysis and Visualization. We can analyze the data deeper and deeper and extract extra insights that can help better understand the data. This analysis can sometimes be a breakthrough for companies. For example, in stock-market data, if a data scientist analyzes data and finds a pattern in which the market goes up and down, the company can make an unimaginable profit there. But this requires experience with data, and that's why every company expects candidates to have hands-on experience in Data Analysis.

In the Roles and Responsibility section, we saw "Presentation with extraordinary visualization techniques to report the results." This is where visualization libraries in Python, like Matplotlib, Seaborn, and Plotly, can help a lot. Many companies directly mention these libraries in their Required Skills section and expect candidates to have proficiency in using them.

Step 5: Getting familiar with Statistics, Probability, and Machine Learning Techniques

Statistics and probabilities are essential math skills for data scientists. They form hypotheses about the data and validate them using statistical information. If the probability of an event falls below a certain level, the hypothesis is rejected. Proficiency in probability, applied mathematics, and statistics is highly valued in the field. In particular, a strong understanding of topics such as general probability, the probability distribution (continuous and discrete), general statistics, and linear algebra is considered ideal for a data scientist.

Data scientists use machine learning techniques when it is challenging to uncover patterns in data. They feed the machine input and output data, and the machine finds the function that fits it. Machine learning also solves previously unsolvable problems due to the inability to execute high-end operations. With recent advancements, machine learning has become highly valuable and is a sought-after skill for data scientists.

Step 6: Applying ML and Data Science techniques to the open-source dataset

Hands-on experience is a must thing to have in Data Science and ML field. Earlier, one of the biggest hurdles was the "availability of dataset," but nowadays, we can find many open-source datasets on which data scientists can practice their skills. Some of those sources are:

  • Kaggle dataset: It is a hub of a wide variety of datasets, including the fields like computer science, environment, agriculture, NLP, and many more. We can easily find complete Machine Learning projects, including data preprocessing and analysis on these datasets.
  • Government Datasets: The government publishes data regarding air quality, irrigation percentage, atmospheric conditions, etc. We can use such datasets to build projects like Prediction of soil fertility, Weather forecasting, Probability of rain, etc.
  • Toy Datasets from Scikit-learn: Most frameworks these days provide free datasets for learners to practice their skills. The scikit-learn framework also provides toy datasets like IRIS flower type classification, digit recognition, cancer prediction datasets, and many more. We can use these datasets to analyze data with various approaches and publish our extra insights.

With the help of these datasets, learners can solve some industrial projects to gain experience in relevant skills and algorithms. For example:

  1. Email spam non-spam filtering
  2. How Uber uses ML?
  3. Building Recommender System using ML

Step 7: Make a Resume and Apply for Internships or fresher positions

Once some projects are completed, one can make a detailed resume mentioning all the relevant projects and the skills utilized to build those projects. It attracts the attention of interviewers and increases the chances of getting shortlisted. Also, we are making resumes to sit for the Data Scientist positions; hence make sure it reflects that we know the meaning of Data Science. For example, we can include small visualizations in our resume to make it easier to read. Some of the example templates are:

  1. The pie chart in a resume:
  2. The bar graph in a resume:

Once the resume is formed and shortlisted, do prepare for the interview. We have covered the process of any ML interview in a separate blog. Learners can generalize the questions in this blog to cover the specific project they have mentioned in their resumes.

After finalizing the resume, one can start applying for internships or fresher positions in the IT industry. Openings can be found on platforms like Linkedin, Job section, Indeed,, Hirist, TopHire, etc. Please read the job description carefully for the role and try to match your expectations from the employer.


Data Science is a rapidly growing career path, with many companies amassing large amounts of data and seeking Data Scientists for roles such as model building, data analysis, data preprocessing, data engineering, and more. This article provides a 7-step guide to becoming a successful Data Scientist and securing a career in the field. We hope you find the information valuable and engaging.

Enjoy continuous learning!

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