On top of being high-level analytical thinkers, Data Scientists have to be effective communicators, leaders, and team members. This is because they often exist in business settings as well.
Here is a step-by-step roadmap to becoming a Data Scientist in 2022.
Table of Contents
Data Science Roadmap
In this step-by-step roadmap to learning data science, at each step, we will also provide resources to help you learn.
Without any further ado, let’s get started!
Here is the order in which you can effectively start learning Data Science:
Python
If you are a complete novice with no programming knowledge whatsoever, Python is the best way to start.
Knowing Python will take you one step closer to learning data science.
Why learn Python first? Because Data Science is all about implementation. And if you don’t have programming knowledge, you can’t implement anything.
Now you might be thinking, “How much Python should I learn at this step?”
At this step, only learn Python Basics. So that you can code in Python.
Following listed are a few resources for learning the Python basics for Data Science:
- Python Certification Course by Intellipaat
- Data Science with Python Course by Intellipaat Academy
Math and Statistics
To pursue data science, one should have a sound knowledge of mathematics and statistics.
Statistics help to determine which algorithm is suitable for a specific problem.
It includes statistical tests, distributions, and maximum likelihood estimators, which are essential in data science.
Statistics also help with counting, normalizing, obtaining distributions, and finding out the mean of the input feature and its standard deviation.
Data science requires mathematical study because machine learning algorithms, analysis, and discovering insights from data require math. While it is not the only requirement for a data science career path, it’s often one of the most important ones.
Following are the resources for learning statistics and maths:
- Statistics for Data Science Couse by Intellipaat
- Data Science Math Skills by Coursera
Python Libraries
Data Scientists have to deal with data. Python has a rich set of libraries that help with data manipulation, data analysis, and data visualization. These collections of pre-existing functions and objects can be imported into a script to save time.
Following are some of the Python libraries that Data Scientists work with:
- Numpy: It is used to perform numerical operations on data. NumPy enables you to convert any data into numbers. Whenever data is not in the numerical form, you can use NumPy to convert it into numbers.
- Pandas: it is an open-source manipulation and data analysis tool. You can also work with dataframes using Pandas.
- Matplotlib: With matplotlib, you can draw graphs and charts of your findings. It is easier to understand the results when they are represented as a graph or a chart.
- Scikit-Learn: Scikit-Learn contains various machine learning modules and algorithms that help in cross-validation, pre-processing, etc.
Following are the resources for learning Python Libraries:
- Numpy Tutorial by Intellipaat
- Python Pandas Tutorial by Intellipaat
- Matplotlib Python Tutorial by Intellipaat
- Scikit-Learn using Python by Intellipaat
SQL Skills
Brushing up your SQL Skills will help you learn how to store and manage data in a database.
While data manipulation can be done using both SQL and Pandas, there are some data manipulation tasks that can be performed easier in SQL.
Following are the resources for learning SQL:
- SQL Training and Certification Course by Intellipaat Academy
- SQL Training by Intellipaat.
Machine Learning Algorithms
Once you’ve learned Python libraries, you need to learn Machine Learning concepts.
You need to learn Machine Learning basics along with the different types of Machine Learning algorithms – Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
You can check out the following resources to learn Machine Learning:
- Machine Learning Tutorial by Intellipaat
- Machine Learning Tutorial by Intellipaat
- Machine Learning Course by Intellipaat
First Machine Learning Model with Scikit-Learn
After you’ve learnt data analysis, manipulation, and visualization, you need to learn how to predict and find interesting patterns from data. Now, you can start building your first Machine Learning Model.
Scikit-learn contains a lot of useful Machine Learning algorithms that are ready-to-use. You need to experiment with various Machine Learning algorithms.
Look for a Machine Learning problem, use data, apply different Machine Learning algorithms, and identify the algorithm that gives the best results.
Data Science Competitions
Once you’re through with the previous steps, it’s time to practice and assess your hold on Data Science skills.
The best way to do that is by participating in competitions. These will help you become more proficient in Data Science.
Kaggle is one of the most prominent platforms for Data Science. It has several competitions according to your knowledge level.
You can start with a basic-level competition like Titanic. As you start gaining more confidence, you can advance to higher levels.
If you want to expand and solidify your skill sets with hands-on experience, joining a Data Science course is highly recommended.
Following is a list of platforms for Data Science competitions:
- DrivenData
- CodaLab
- Iron Viz
- Topcoder.
Conclusion
If you follow the steps given above and practice the required skills, you’ll be able to learn Data Science with Python easily. The important thing to remember is to keep practicing your skills.
Keep looking for new challenges and try to solve them. These challenges and projects will also enhance your portfolio.
