Start with the Overview of Data Science. Read some Data Science related blogs and also research some Data Science-related things.
For example;

1. Read blogs on Introduction to Data Science,
2. Why to choose data science as a career,
3. Industries That Benefits the Most From Data Science,
4. Top 10 Data Science Skills to Learn in 2020, etc,

And make a complete mind makeup to start your journey in Data Science. Make yourself self-motivated to learn Data Science and build some awesome projects on Data Science. Do it regularly and also start learning one one new concept on Data Science. It will be very better to join some workshops or conferences on Data Science before you start your journey. Make your goal clear and move on toward your goal.

1) Mathematics
Math skill is very important as they help us in understanding various machine learning algorithms that play an important role in Data Science.

Part 1:

• Linear Algebra
• Analytic Geometry
• Matrix
• Vector Calculus
• Optimization

Part 2:

• Regression
• Dimensionality Reduction
• Density Estimation
• Classification

2) Probability
Probability is also significant to statistics, and it is considered a prerequisite for mastering machine learning.

• Introduction to Probability
• 1D Random Variable
• The function of One Random Variable
• Joint Probability Distribution
• Discrete Distribution;
• Binomial (Python | R)
• Bernoulli
• Geometric etc
• Continuous Distribution;
• Uniform
• Exponential
• Gamma
• Normal Distribution (Python | R)

3) Statistics
Understanding Statistics is very significant as this is a part of Data analysis.

Introduction to Statistics

• Data Description
• Random Samples
• Sampling Distribution
• Parameter Estimation
• Hypotheses Testing (Python | R)
• ANOVA (Python | R)
• Reliability Engineering
• Stochastic Process
• Computer Simulation
• Design of Experiments
• Simple Linear Regression
• Correlation
• Multiple Regression (Python | R)
• Nonparametric Statistics;
• Sign Test
• The Wilcoxon Signed-Rank Test (R)
• The Wilcoxon Rank Sum Test
• The Kruskal-Wallis Test (R)
• Statistical Quality Control
• Basics of Graphs

4) Programming
One needs to have a good grasp of programming concepts such as Data structures and Algorithms. The programming languages used are Python, R, Java, Scala. C++ is also useful in some places where performance is very important.

• Python:
• R:
• DataBase:

5) Machine Learning
ML is one of the most vital parts of data science and the hottest subject of research among researchers, so each year, new advancements are made in this. One at least needs to understand the basic algorithms of Supervised and Unsupervised Learning. There are multiple libraries available in Python and R for implementing these algorithms.

• Introduction:
• Intermediate:

6) Deep Learning
Deep Learning uses _TensorFlow _and _Keras _to build and train neural networks for structured data.

• Artificial Neural Network
• Convolutional Neural Network
• Recurrent Neural Network
• TensorFlow
• Keras
• PyTorch
• A Single Neuron
• Deep Neural Network
• Stochastic Gradient Descent
• Overfitting and Underfitting
• Dropout Batch Normalization
• Binary Classification

7) Feature Engineering
In Feature Engineering, discover the most effective way to improve your models.

• Baseline Model
• Categorical Encodings
• Feature Generation
• Feature Selection

8) Natural Language Processing
In NLP, distinguish yourself by learning to work with text data.

• Text Classification
• Word Vectors

9) Data Visualization Tools
Make great data visualizations. A great way to see the power of coding!

1. Excel VBA
2. BI (Business Intelligence):

• Tableau
• Power BI
• Qlik View
• Qlik Sense

10) Deployment
The last part is doing the deployment. Definitely, whether you are fresher or 5+ years of experience, or 10+ years of experience, deployment is necessary. Because deployment will definitely give you the fact that you worked a lot.

• Microsoft Azure
• Heroku
• Google Cloud Platform
• Flask
• DJango

11) Keep Practicing
“Practice makes a man perfect,” which tells the importance of continuous practice in any subject to learn anything.

So keep practicing and improving your knowledge day by day. Below is a complete diagrammatical representation of the Data Scientist Roadmap.