A Roadmap To Becoming An Expert Data Scientist

A Roadmap To Becoming An Expert Data Scientist

A Survey Conducted By Harvard Business Review Shows That Data Scientist Is Known As The Most Attractive Job Of The 21st Century.

In the world of information technology, the era of Big Data emerged when organizations faced petabyte and exabyte-scale data. Hence, organizations faced severe problems in data management and organization during the 2010s.

Thanks to popular frameworks such as Hadoop and similar examples focusing on data processing, the problem of storing, organizing, and rendering data has largely been solved. Frameworks that created a solid foundation for data science.

This problem has caused data science to be used differently in different industries. Hence, it is essential to learn what data science is and how this science can be used to achieve added value.

What is data science?

The first question that arises is, what is data science? Data science can be defined in many ways, but at its core, it tries to use data to solve real-world problems. Data science analyzes raw data using statistics and machine learning techniques to conclude the information. This definition is simple on the surface, but it has a profound and broad meaning, and that is why we must say that data science is a broad field with diverse applications.

In short, we must say that data science is based on the following sciences and concepts:

  •  Statistics, computer science, mathematics
  • Data cleaning and formatting
  •  Data visualization

Once we understand the broad applications of data science, other questions come to mind. Should we know all the concepts by attending a training course, reading a book, watching various online tutorials, or learning data science by doing projects? For example, how to learn data science, where to start, and what topics to study.

 In this article, we will examine all these things in detail. Figure 1 shows the roadmap and set of skills needed by a data scientist, which we intend to mention in this article briefly.

figure 1

Why should we use data science?

If you are planning to enter the fascinating world of data science, you should first consider your goal in entering this field. Data science analyzes raw data using statistics and machine learning techniques to gain detailed insights about the information. Therefore, before you draw a road map for this knowledge, you must have a clear goal in mind and know why you intend to learn data science.

Are you interested in this science only because of the hype surrounding it? Do you need to learn this science to do university projects, or are you thinking about it for a long-term career? Do you want to change your current job and enter the world of data science?

As you can see, first, you need to define the goal. Why do you want data science?

Learn For example, if you want to learn data science for your college projects, learn the basics of data science. Or if you’re providing a long-term job for yourself, you should think about learning professional and advanced topics and learn all the details of this science.

How to learn data science?

Typically, data scientists have a variety of educational backgrounds and work experience, and it is not the case that the above science is only available to computer science or information technology graduates. However, you must be proficient in the following four key areas to perform the assigned duties. These four areas are as follows:

  •  Domain Knowledge
  •  Math Skills
  •  Computer Science
  •  Communication skill

Domain knowledge

Most people believe that domain knowledge is not very important in data science, while it is one of the essential topics in this field. To clarify the discussion, let us refer to an example. Suppose you want to become a data scientist in the banking industry and have good knowledge about banking things like stock trading, financial information, etc. In that case, your chances of success in this field are double because banks prefer to hire an ordinary applicant who does not know this field. Go to people who specialize in data science and have practical knowledge about their industry.

Mathematical skills

Linear algebra, multivariate calculus, and optimization techniques are three essential components that a data scientist needs. These skills help us understand various machine-learning algorithms that play a crucial role in data science. Similarly, understanding statistics is necessary because part of the process of data analysis is related to statistical issues. Similarly, probabilistic learning is also crucial and a prerequisite for machine learning.

computer science

There are many topics to learn in computer science, but when it comes to programming languages, one of the most critical questions is whether Python or R is better for data science. Both provide a rich set of libraries for implementing complex machine learning, visualization, and data-cleansing algorithms. There are several reasons to choose each of these languages for data science.

I suggest learning both programming languages to become a successful data scientist. Apart from the programming language, there are other skills in the field of computer science that you should think about learning, including the following:

  •  Basics of data structure and algorithms
  •  Structured Query Language (SQL)
  •  MongoDB database
  •  Linux operating system
  •  Git version control tool
  •  Distributed computing
  •  Machine learning, deep learning, etc

communication skill

Communication skill refers to both areas of written and verbal skills. What happens in a data science project is that after the analysis is concluded, the project needs to be shared with others. Sometimes, this report may be sent to the CEO and board of directors, or it may be a blog post that should be posted on the company’s website. However, in most cases, the above report is given to people with different expertise.

In general, a data science project emphasizes the interaction of different people in a group with each other. Therefore, having communication skills is essential to become a data scientist.

The roadmap you need to become a practical data science professional

The road map for learning applied skills refers to the set of skills that people need to master a science. In most cases, the roadmap is detailed, and data science is no exception. So let’s start with an overview of data science.

We suggest reading some data science blogs and researching data science-related topics to get started. For example, read blogs on Introduction to Data Science, Why to choose Data Science as a career, Industries that will benefit the most from Data Science, Top 10 Data Science Skills to learn in the coming years, etc., to get an initial mindset in this Find context.

Not a bad idea to have a few outstanding projects based on thatThey is data science, and their information is publicly published. Also, attending some workshops or conferences related to data science is suggested before starting your journey into this field.


Mastery of mathematical topics and fundamental principles is crucial because it helps us understand the performance of various machine learning algorithms that play an essential role in data science. In general, in the subject of mathematics, you should think about learning the following:

  •  Linear Algebra
  •  Analytic geometry
  •  matrix
  •  Differential and integral calculus
  •  regression
  •  Dimension reduction
  •  Density estimation
  •  Classification


Probability is one of the most important topics in statistics, which is very important and is used to calculate estimates, maximums, and minimums. Probability should be described as a prerequisite for machine learning and data science. To learn probability, you should consider learning the following topics:

  •  One-dimensional random variable
  •  the function of a random variable
  •  joint probability distribution
  •  Discrete distribution
  •  two sentences
  •  Bernoulli calculations
  •  Continuous distribution
  •  continuous uniform distribution
  •  View
  •  gamma
  •  Normal distribution


For the science of statistics, where a large part of data analysis is based on this science, you should think about learning the following:

  •  Descriptive Statistics
  •  Random samples
  •  Sampling distribution
  •  Parameter estimation
  •  Testing hypotheses
  •  Analysis of variance
  •  Random process
  •  Simple and multiple linear regression
  •  Correlation
  •  Non-parametric statistics
  •  Wilcoxon Signed-Rank, Wilcoxon Rank Sum, and Kruskal-Wallis tests
  •  statistical quality control


data scientist should understand programming concepts such as data structures and algorithms well. For this reason, he must know enough about the programming languages ​​used in this field, such as Python, R, Java, and Scala. C Plus Plus programming language also has an acceptable performance in some areas.

For the Python programming language, it is essential to learn concepts such as lists, sets, tuples, dictionaries, functions, NumPy, Pandas, Matplotlib/Seaborn, etc. For R programming language, it is recommended to learn the basics of this language, vectors, lists, data frames, matrix, array, functions, dplyr, ggplot2, tidyr, and Shiny packages.


Unfortunately, the data that a data scientist receives does not come from a specific source; sometimes, they have to collect and refine the data themselves. In most cases, this is not the responsibility of data scientists. However, for databases, consider learning SQL, MongoDB, non-relational databases, data structures (time series), searching the web, and storing information in databases.

machine learning

Machine learning (ML) is one of the most critical skills in data science the hottest technology is artificial intelligence. So many improvements are made in this field every year; for this reason, you should have sufficient knowledge about the basic paradigms of this field, such as supervised and unsupervised learning. Fortunately, good libraries for the Python and R programming languages ​​are available to developers that can be used to implement these algorithms.

The working mechanism of machine learning is that you must first understand how a model works, explore the topic of fundamental and visual data discovery, design your first machine learning model, and learn the model validation process, Overfitting and underfitting issues. & Overfitting, increase your knowledge of random forests, learn how to work with the SkateLearn library, learn about System Missing Value, Handling Categorical Variables Learn, learn techniques for building data transmission lines and learn cross-validation.

Considering that machine learning is an essential skill that a scientist and data science should know about, in addition to the above, you should think about learning more advanced topics like K-means clustering, decision tree, K nearest neighbor, etc. Fortunately, most machine learning algorithms can be implemented using R or Python.

Interestingly, Python libraries perform better in this field. What you should think about learning is the ability to understand the algorithms required based on the type of data and what a model is supposed to do.

Data management and data preparation

Data plays a vital role in the life of a data scientist. So you need to be proficient in data management, including data extraction, transformation, and loading. It means you need to extract data from various sources, convert it into the format required for analysis, and finally load it into the data warehouse. Multiple frameworks, such as Hadoop, Spark, etc., are available to manage this data. Once the data management process is over, the next step is to think about data preparation. Data preparation means that stored data must be refined and integrated before being analyzed to gain actionable insights.

Data Intuition

Don’t underestimate the power of data intuition. This non-technical skill sets a data scientist apart from a data analyst. Data intuition involves finding patterns in data that are not apparent at first glance. More precisely, they are like finding a needle in a haystack. Data intuition is not a skill that can be easily taught but acquired through experience and continuous practice. However, it is a skill that sets you apart as a data scientist.

Deep learning

AI engineers use TensorFlow and Cross for deep learning and building neural networks based on structured data. Typically, a data scientist doesn’t need to learn deep-learning topics. Still, if you’re thinking about advancing your career in the future, we suggest investing in learning neural network topics early on.

For this purpose, you should think about learning topics such as artificial neural networks, convolutional neural networks, recurrent neural networks, TensorFlow libraries, cross, PyTorch, random gradient descent, dropout batch normalization, and binary classification.

Feature engineering

In the topic of Feature Engineering, the goal is to identify the most effective solution to improve the performance of models. For this purpose, you should consider learning Baseline Models, Classified Coding, and Feature Selection.

Natural Language Processing

Natural language processing means the ability of computers and machines to understand dialogues and conversations made by humans with machines. The key to success in this field is the ability to work with textual data. In this section, it is suggested to think about learning concepts such as text classification and word vectors.

Valuable tools for data visualization

Data visualization is a great way to demonstrate skills visually. More precisely, it shows your coding skills. In this area, it is suggested to focus on learning topics such as Excel VBA, business intelligence, Qlik View, and Qlik Sense.


The last skill in this area is deployment. It doesn’t matter if you are a novice or an experienced person. Once you create a model, you eventually need to be able to deploy it. The establishment shows that you have completed the assigned tasks best.

Usually, experts do the deployment process in cloud hosts, and for this purpose, they use options such as Microsoft Azure, Heroku, Google Cloud Platform, etc. Of course, there are in-house options out there. It would be best if you researched the capabilities they offer before deploying.