Roadmap to Data Scientist — the ultimate path to become a Data Scientist.

“Learning how to do data science is like learning to ski. You have to do it.”

— Claudia Perlich

Overview

Data science is the study of using domain knowledge, programming skills, math and statistics to get meaningful insights out of data. Practicing data science involves applying machine learning algorithms to a number of data types, including text, images, video, and audio. This creates artificial intelligence systems (AI) that can perform tasks typically performed by humans. As a consequence, these systems generate business insights that analysts and business users can translate into actionable business value.

In simple words, a Data Scientist is one who practices the art of Data Science. Data scientists are those who crack complex data problems with their strong expertise in certain scientific disciplines using their knowledge of mathematics, statistics, computer science, etc.

The purpose of a Data Scientist is to use large amounts of data to research a problem and to then apply statistical techniques to draw meaningful and valuable conclusions from that data. A Data Scientist uses advanced modeling techniques such as machine learning and deep learning. Additionally, a data scientist must communicate their results to various stakeholders, such as marketing, sales, engineering, etc., and suggest solutions.

There are multiple kinds of data science roles:

• Data Scientist?— A Data Scientist is knowledgeable about almost everything in the realm of data science, including data collection, processing, analysis, presentation, and extraction of decision-making information.
• Data Engineer?— The Data Engineers are responsible for the most technical aspects of the system. These include designing, building, and maintaining the data pipelines. The system is designed to gather data from various sources and store it as efficiently as possible.
• Data Analyst?— Most people these days have start assuming that Data Analyst is just another name for Data Scientist but there is a hell of difference between them! Analysis of data in the data warehouse is undertaken by the data analysts, who also create queries, create data visualizations, and develop reports with the business to uncover insights.

There are many more multiple kinds of Data Science roles that might be almost impossible to cover in this article. You can have a look at them on?https://www.mygreatlearning.com/blog/different-data-science-jobs-roles-industry/

. . .

Let’s talk about the steps required to be a data scientist —

Start with learning the basics of Python or R

Most people tend to ignore this part and don’t focus that much on the basics of Python or R. However, if your base is really strong in one of these languages, then the advanced stuff would be pretty easy.

In data science, Python and R are the most commonly used languages. In most cases, one can replace the other, but they have distinct uses.

The basics of Python include the following:

• Setting up a Python Developer Environment
• Learning Various Python Data Types and Python Data Structures
• Control Flow statements
• Different types of loops and nested loops
• Functions, modules and imports.
• Exception Handling
• Object-oriented programming (OOP) (This includes Encapsulation, Inheritance, Polymorphism, Abstraction)

You could look at my some of my Python Lectures for beginners on my GitHub Repository:?https://github.com/Shahzeb-A/Python-Lectures-for-Beginners

Mathematics & Statistics

Mathematics and statistics are really important part of Data Science and most of the concepts revolve around them. However it does not mean that you should be particularly an expert in both or either of them. I would suggest covering the basics from Khan Academy which would be sufficient to continue.

Statistics and probability :?https://www.khanacademy.org/math/statistics-probability

Data Cleaning

Data cleaning is?the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.

I would that approximately 60 percent of Data Science revolves?around cleaning the data most of the time and the other 40 percent is applying algorithms and training models. This could include removing null data, repeated data, corrupted data, etc.

One should practice data cleaning by practicing with different kind’s of datasets. You could do find some good datasets on?https://www.kaggle.com. Kaggle is an online community platform for data scientists and machine learning enthusiasts.

Kaggle?allows users to collaborate with other users, find and publish datasets, use GPU integrated notebooks, and compete with other data scientists to solve data science challenges

Data Visualization

Data visualization is?the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

The data visualization performed by these data scientists and researchers?helps them understand data sets and identify patterns and trends that would have otherwise gone unnoticed.

In order to visualize data, you should use some of the prestigious data science libraries:

• Pandas ( Python data analysis )?is a must in the data science life cycle
• Matplotlib?has powerful yet beautiful visualizations.
• Seaborn ( Visualize random distributions )

Machine Learning

Machine learning (ML) is?a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

There are various machine leaning algorithms you could apply on your data. Some of them include:

• Linear Regression (Predict the value of a variable based on the value of another variable)
• Logistic Regression ( the probability of an event occurring, such as voted or didn’t vote, based on a given dataset of independent variables.)
• Decision Tree (Tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility)

There are many more machine learning algorithms used but it’s impossible to cover in this article.

You could have a look at them on :?https://www.simplilearn.com/10-algorithms-machine-learning-engineers-need-to-know-article

Conclusion

The learning does not stop at this point. Like any other field, learning continues in data science as well. These skills will set you on the path of data science but there is a long way to go and as time passes by new and new technologies are introduced in Data Science.