How to become a Data Scientist (Step by Step guide)

There are many tutorials and blogs which provides you the best path for becoming an aspiring data scientist. The most common ways to enter into a data science field is :

Step 1 : Choose A Programming Language (Python / R)

The first stage in the Data Science Journey is to become acquainted with a programming language. Between the two, Python is the more popular coding language, with the majority of Data Scientists using it. If you are ask for my suggestion then obviously Learn Python because it's simple to use, adaptable, and comes with a number of built-in libraries, including Numpy, Pandas, MatplotLib, Seaborn, Scipy, and others.

NOTE: Essential Python variables, data types, OOPs ideas, Numpy, Pandas, Matplotlib, and Seaborn should all be learned when learning Python.

STEP 2. Statistics

Knowing statistics and probability is as important as having salt in your food for becoming a Data Scientist. Knowing them will aid data scientists in better interpreting massive data sets, extracting insights from them, and analysing them.

NOTE: The concepts of Mean, Median, Mode, Range, Variance, Standard Deviation, Graphs or Plotting, Populations, and Samples are all covered in statistics.

STEP 3: Learn SQL

Large databases are accessed and communicated with using Structured Query Language (SQL). Understanding the various types of normalisation, constructing nested queries, employing co-related inquiries, group-by, conducting join operations, and extracting data in raw format should be prioritised. After that, the data will be cleaned using either Microsoft Excel or Python libraries.

NOTE: In SQL, you should be familiar with table creation, data inserting, data updating, data deletion, and fundamental query operations.

STEP 4. Data Cleaning

When a Data Scientist is assigned a project, he or she spends the majority of their time cleaning the data collection, deleting undesired values, and dealing with missing values. It can be accomplished by utilising Python libraries such as Pandas and Numpy.

It's also a good idea to know how to work with data in Microsoft Excel.

NOTE: You should be familiar with basic data filtering and sorting in Microsoft Excel, as well as Functions and Formulas, Vlookup, Pivot tables and charts, and Tables, among other things.

STEP 5: Exploratory Data Analysis

When it comes to data science, exploratory data analysis is critical. The data scientist's responsibilities include identifying data patterns, evaluating data, identifying relevant trends in data, and extracting important insights, among other things, using a variety of graphical and statistical tools, such as:

A) Numpy and Pandas for data analysis

B) Data Manipulation

C) Data Visualization

STEP 6: Learn Machine Learning Algorithms

"Machine learning is a method of data analysis that automates analytical model construction," according to Google. It's a subset of artificial intelligence predicated on the premise that computers can learn from data, recognise patterns, and make decisions with little or no human intervention."

It is the most important part in a data scientist's life cycle, as it requires the creation of numerous models utilising machine learning techniques, as well as the ability to forecast and come up with the best answer to any problem.

Step 7: Practice On Analytics Vidhya and Kaggle

Now that you've learned the fundamentals of Data Science, it's time to put them into practise. Many online platforms, such as Kaggle and Analytics Vidhya, may give you with hands-on experience with beginner and advanced data sets. They can assist you in comprehending various machine learning algorithms, analysing strategies, and so forth.

You can utilise the approach outlined below to learn how to use these platforms efficiently:

1. You can begin by downloading the datasets, examining the data, and putting all of the skills you've learned into practise.
2. Then you may look through other people's notes to see how they solved a problem or derived insights from the data. (This strategy will undoubtedly increase your self-assurance and assist you in improving your expertise.)
3. After you've gained enough confidence, you can enter competitions hosted by Kaggle and Analytics Vidhya. This will not only help you improve your Data Science skills, but also your understanding of Data Science.

Book I prefer to start your journey in the field of data science is :- 

Data Science from Scratch by Goel Erus

You can follow this link to buy this book : https://amzn.to/3pdqcZP

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