How to Become Data Scientist – A Complete Roadmap

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The discipline of Data Science is strong, expanding quickly, and has much-unrealized potential. According to LinkedIn's Emerging Jobs Report, during the next seven years, the market is predicted to increase dramatically, rising from $37.9 billion in 2019 to $230.80 billion in 2026.

Consequently, data science should be the starting point for prospective IT professionals looking for a long-term career. But picking up a new subject might be difficult. By developing and putting into action an effective educational strategy, or roadmap, the challenge might be lessened.

The information required to develop a data science roadmap for 2022 is provided in this article. We will define a data science plan, go through its components and milestones, go over how to measure your progress on the data science roadmap, and more.

Data science: What is it?

What is Data Science, then, is the first question that pops into your head? Data science is the utilization of data to produce answers to questions, even if it can mean various things to different people. Data science is the study of how to use statistics and machine learning to analyze raw data to make inferences about that data. This definition is relatively broad since data science is a discipline that must be described as moderately broad. Data Science in the USA is one of the most popular programs.

To put it succinctly, data science involves:

  • Mathematics, statistics, and computer science
  • Cleaning and formatting data
  • Visualization of data

Everyone is aware of how popular data science is nowadays. The challenges that now need to be answered are: Why Data Science, and How to Start? Where do I begin? What subjects ought to be covered? , etc. Do you need to read a book to master all the ideas, should you use online courses, or should you study Data Science by working on projects related to it? We will thus go into depth about each of these topics in this post.

Why Study Data Science?

Therefore, one should have a specific purpose in mind as to why they want to study data science before diving into the full Roadmap of data science. Would you consider it for: Is it for your college coursework? or is it for your long-term professional success? Or are you interested in making the switch to data science? Set a clear objective at first. Why did you choose to study data science? If you want to apply data science for college coursework, for example, mastering the basics is enough.

Similarly, if you want to create a long-term career, you should learn professional or advanced abilities. You must carefully review all the qualifications. The motivation for learning data science is entirely up to you; make that decision now.

Methods for Learning Data Science

Most data scientists should be skilled in, or in an ideal situation, be masters in, four essential areas. Data scientists often come from a variety of educational and professional backgrounds.

  • Domain expertise
  • Math abilities
  • Computing Science
  • Skill in Communication
  • Domain expertise

The majority of people are erroneous when they say that domain knowledge is not important for data science. Think about the following example: You will benefit greatly if you are interested in working as a data scientist in the banking sector and have a wealth of industry knowledge, such as expertise in stock trading, finance, etc. The bank itself will prefer you above other candidates.

Math abilities

The importance of these three ideas—Linear Algebra, Multivariable Calculus, and Optimization Technique—can be seen in how they help us understand the many machine learning algorithms that are essential to data science. In a similar vein, statistics are essential to understand since they are employed in data analysis. Additionally important to statistics, the probability is regarded as a must for understanding machine learning.

Computing Science

In the field of computer science, there is a lot to learn. But one of the key inquiries that arise with programming languages is:

Since both offer a robust collection of libraries to perform the sophisticated machine learning algorithm, visualization, and data cleansing, there are many factors to consider when deciding which language to choose for data science. For further information, see R vs. Python in Data Science.

In addition to learning a programming language, you should master the following computer science skills:

  • Fundamentals of algorithms and data structures
  • SQL
  • MongoDB
  • Linux
  • Git
  • Computerized distribution
  • Deep learning, machine learning, etc

Skill in Communication

It covers both spoken and written communication. In a data science project, the study must be explained to others when findings from the analysis have been reached. This can occasionally be a report that you provide to your team or employer at work. Sometimes it may be a blog entry. Generally, it is a presentation to a group of employees. Whatever the case, communicating the research's findings is a necessary part of every data science endeavor. Therefore, having good communication skills is a need for being a data scientist.

What Is a Roadmap for Data Science?

To answer this topic simply, let's first define what a "roadmap" is. Maps are strategic plans that identify a goal or desired result and list the key actions or milestones needed to get there.

In contrast, Data Science is described in this article as:

  • A branch of data science that deals with semi-structured, structured, and unstructured data. Data preparation, analysis, and cleansing are just a few of the processes involved.
  • Combining statistics, arithmetic, programming, and problem-solving skills, data science is the practice of cleaning, preparing, and aligning data. It also involves the capacity to look at things differently.
  • A strategy plan intended to assist the prospective IT professional in learning about and succeeding in the subject of data science is thus visualized in a data science roadmap.

Let's examine this data science road plan in more detail.

Studying programming and/or software engineering

You need to have a strong foundation before you start your data science adventure. Software engineering or programming expertise and experience are needed in the data science area. Learning at least one programming language is recommended, which is from Python, SQL, Scala, Java, or R. Any programming language is acceptable, although owing to Python's popularity, we advise using it. This is a common request made by businesses as a mandatory necessity.

  • Study the fundamentals of Python
  • Discover Numpy
  • Study up on pandas.
  • Study Matplotlib/Seaborn
  • Discover the time complexity of algorithms
  • Discover how to store data in a database.

Included Programming Topics

Common data structures (such as dictionaries, data types, lists, sets, and tuples), searching and sorting algorithms, logic, control flow, creating functions, object-oriented programming, and how to use third-party libraries are all things that data scientists should get familiar with.

Aspiring data scientists should also be comfortable using Git and GitHub-related tools like version control and terminals.

Finally, SQL programming should be known to data scientists.

Studying GitHub and Git

To learn Git and GitHub, there are a lot of materials accessible. Take a look at a Git lesson for instance, or enroll in GitHub and Git training here.

Project development and problem-solving

After becoming functionally comfortable with the aforementioned ideas, put your newfound knowledge to use by taking on building tasks like writing Python scripts that extract data or developing a straightforward web application that bans undesired websites.

Studying Data Cleaning and Collection

Finding useful data that addresses issues is a common task for data scientists. They get this information from a wide range of resources, including databases, APIs, public data repositories, and even scraping if the site lets it.

The information acquired from these sources is rare, though, usable. A multi-dimensional array, data frame modification, or applying scientific and descriptive calculations are some methods that may be used to clean and prepare data before it is used. Libraries like Pandas and NumPy are frequently used by data scientists to transform raw, unformatted data into data that is suitable for analysis.

Selected Projects for Data Collection

Choose a publicly available data collection, create a list of inquiries relevant to the dataset's field, then practice manipulating data with Pandas or NumPy to obtain the answers.

Alternatively, collect data from a public-accessible website or API (like Quandl, TMDB, or the Twitter API) and combine the data from many sources into a single database table or file for storage.

How to Study Exploratory Data Analysis, Business Acumen, and Storytelling?

It's time to advance to the data analysis and narrative stages of your data science plan. Data analysts, who have a close relationship with data scientists, analyze data to derive conclusions, then present their results to management in simple language and graphic representations. Python is capable of handling data visualization and large data, however, certain other tools were created specifically for the task at hand. Once you have begun using Python to address a few data science challenges, you should investigate these tools to learn what they have to offer. These tools consist of:

  • Tableau
  • Excel (& VBA)
  • Hadoop
  • AWS Offerings

The aforementioned tasks as they relate to storytelling call for expertise in data visualization (plotting data using libraries like Plotly or seaborn) and good communication abilities. Additionally, you should understand:

  • Business Savvy: Get comfortable posing inquiries that focus on financial indicators. Write presentations, business-related blogs, and reports that are succinct and straightforward.
  • Dashboard Development: This topic involves creating dashboards with Excel or specialist software like Power BI and Tableau that summarise or aggregate data to assist managers in making wise decisions.
  • Exploratory Data Analysis: This subject matter includes developing research questions, formatting, filtering, dealing with missing values, addressing outliers, and doing univariate and multivariate analyses.

An analysis of data project

Use information from previous censuses or financial/health/demographic databases to construct a formula for producing successful movies by performing an exploratory study of movie datasets.

Why You Should Study Data Engineering

In large data-driven enterprises, data engineering aids the Research and Development teams by ensuring that clean data is easily accessible for research engineers and scientists. If you want to concentrate mostly on the statistical side of things, you can skip this part even though data engineering is a completely distinct area.

Building effective data structures, simplifying data processing, and sustaining sizable data systems are all duties of a data engineer. Data engineers use SQL, Shell (CLI), and Python/Scala tools to automate file system tasks, build Extract/Transform/Load pipelines, and accelerate database activities into a high-performance resource. The implementation of these data architectures is frequently the responsibility of data engineers, who unavoidably need knowledge of cloud service providers like Amazon Web Services, Microsoft Azure, and Google Cloud Platform, among others.

How to Learn Applied Mathematics and Statistics

The bulk of interviews in data science focus on inferential and descriptive statistics, which are crucial to the discipline. Mathematics and statistics lay the foundation for a greater understanding of how algorithms work. Not programming, then why? Because learning arithmetic requires practice, and programming requires an aptitude for numbers and logic.

  • Read a book like Hines to learn the fundamentals of statistics.
  • Find out what dy/dx means!
  • Learn about gradient descent and optimization. This playlist is useful for learning the fundamentals of gradient descent.
  • Learn how to plot basic functions in Excel!
  • Learn the fundamentals of probability distributions with a focus on the normal distribution. Hines is a useful book to learn these fundamentals from.
  • Machine learning mathematics

Other than that, you should concentrate on mastering the following at this point of your data science roadmap:

  • Learn about the variability and location estimates (mean, median, mode, trimmed statistics, and weighted statistics) used to explain data in descriptive statistics.
  • Developing business metrics, conducting A/B testing, creating hypothesis tests, and assessing gathered data and experiment outcomes using confidence intervals, p-values, and alpha values are all aspects of inferential statistics.
  • You can better comprehend gradient, loss functions, and other concepts by taking Linear Algebra and Single and Multivariate Calculus.

Ideas for Statistics Projects

Create a hypothesis based on the average returns or another measure of your choosing after analyzing data such as stock prices or cryptocurrency values. To decide if you can reject the null hypothesis, utilize crucial values in the last step.

By asking your colleagues to respond to questions, interact with apps, or provide answers, you may design and run quick experiments with them. Once you have a good amount of data over a predetermined period, run statistical algorithms on it.

Learn about Machine learning and Artificial Intelligence.

It's time to wrap up your journey by learning about two industries that strongly rely on data science: Artificial Intelligence and Machine Learning, as you near the finish of your data science roadmap. Three categories can be used to categorize these topics:

  • Build self-rewarding systems with the aid of reinforcement learning. Use the TF-Agents library, build Deep Q-networks, discover how to maximize rewards, and more if you want to comprehend reinforcement learning.
  • Regression and classification issues are covered in the subject of supervised learning. The study of polynomial regression, naive Bayes, KNNs, tree models, ensemble models,  multiple regression,  simple linear regression, logistic regression, and multiple regression with logistic regression would be advantageous. In order to complete your training, study evaluation metrics.
  • Unsupervised Learning: Applications of unsupervised learning include dimensionality reduction and grouping. Examine K-means clustering, hierarchical clustering Gaussian mixtures and PCA  in further detail.

You should practice your machine learning principles when you have mastered Python's data manipulation capabilities. Start with this free machine learning course from Google, which is designed just for beginners. The fact that this course is free and covers the exact subjects you need to know to work in the field makes it the greatest option available.

  • Discover Sklearn
  • Develop a neural network using Tensorflow.
  • Use the TensorFlow hub tutorial
  • Get familiar with using the TensorBoard.


Data science has a significant impact on everything from machine learning to data mining in today's IT ecosystem. We offer everything you need to make your data science roadmap journey easy if you want to start a career in data science.

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