Do you want to learn data science with python and looking for **Data Science with Python Roadmap? **If yes, then this article is for you. In this article, you will find a step-by-step roadmap to learn data science with python. Along with that, at each step, you will find resources to learn.

So without any further ado, let’s get started-

**Data Science with Python Roadmap**

**Data Science with Python Roadmap**

So, you have chosen Python programming. **Good Decision!**

Because Python is one of the **most widely used** programming languages in the data science field. Python has many packages and libraries specifically tailored for certain functions such as **pandas, NumPy, scikit-learn, Matplotlib, and SciPy**.

Now let’s see in what order you should start learning data science with Python.

**Step 1- Learn Python First**

If you are a complete beginner and don’t have Python Programming knowledge, then first **learn Python.**

But if you already have Python knowledge, then **you are one step closer to learning data science.**

Why I am suggesting learning 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.

**-Resources for Learning Python-**

**The Python Tutorial (PYTHON.ORG)****Python for Absolute Beginners!****(Udemy)****Python for Everybody**Specialization**(Coursera)****Python 3 Tutorial (SOLOLEARN)****CS DOJO (YouTube)****Programming with Mosh****(YouTube)****Corey Schafer****(YouTube)****Python Crash Course****(Book)**

**Step 2- ****Learn Math & Statistics**

**Learn Math & Statistics**

To learn data science, you should have a good understanding of Statistics and mathematics. Knowledge of statistics will give you the ability to decide **which algorithm is good for a certain problem.**

Statistics knowledge includes** statistical tests, distributions, and maximum likelihood estimators**. All are essential in data science.

Knowledge of Statistics helps you to **count** well, **normalize** well, **obtain distributions**, find out the **mean** of your input feature, and its **standard deviation**.

Mathematics helps you to identify **under-fitting and over-fitting** by understanding the **Bias-Variance tradeoff.**

**-Resources for Learning Statistics & Maths-**

**Basic Statistics****(Online Course)****Statistics and probability****(Khan Academy)****Practical Statistics for Data Scientists****(TextBook)****Data Science: Statistics and Machine Learning Specialization****(Online Course)****Statistics for Data Science****(YouTube Video)****Mathematics for Data Science Specialization****(Online Course)****Khan Academy****Data Science Math Skills****(Online Course)**

**Step 3- ****Familiar with Python Libraries**

**Familiar with Python Libraries**

Now, you need to know how to deal with data. And for this, Python has a rich set of libraries to perform **data manipulation, analysis, and visualization.**

Libraries are the collection of pre-existing functions and objects. You can import these libraries into your script to save time.

Python has the following libraries-

**Numpy-**NumPy will help you to perform**numerical operations**on data. With the help of NumPy, you can convert any kind of data into numbers. Sometimes data is not in a numeric form, so we need to use NumPy to convert data into numbers.**Pandas-**pandas is an open-source data analysis and manipulation tool. With the help of pandas, you can work with data frames. Dataframes are nothing but similar to Excel files.**Matplotlib**– Matplotlib allows you to draw a graph and charts of your findings. Sometimes it’s difficult to understand the result in tabular form. That’s why converting the results into a graph is important. And for that, Matplotlib will help you.**Scikit-Learn-**Scikit-Learn is one of the most popular Machine Learning Libraries in Python. Scikit-Learn has various machine learning algorithms and modules for pre-processing, cross-validation, etc.

**-Resources for Learning Python Libraries-**

**NumPy Tutorial by freeCodeCamp****Exploratory Data Analysis With Python and Pandas****(Guided Project)****Applied Data Science with Python Specialization****by the University of Michigan****NumPy user guide****pandas documentation****Matplotlib****Guide****scikit-learn****Tutorial**

**Step 4- ****Brush Up on SQL Skills**

**Brush Up on SQL Skills**

You should know how to store and manage your data in a database. That’s why you should have an **understanding of SQL.**

You can **manipulate data** using both **SQL and Pandas. **But there are certain data manipulation tasks that can be easily performed using SQL.

That’s why you should know how to use SQL and Python together efficiently.

**-Resources for Learning SQL-**

**Step 5- ****Learn **Machine Learning Algorithms

**Learn**Machine Learning Algorithms

Now, you have learned Python libraries. It’s time to learn **Machine Learning Concepts**.

At this step, you need to learn the basics of Machine Learning and **Types of Machine Learning algorithms**(** Supervised, Unsupervised, Semi-Supervised, Reinforcement Learning**).

You can watch the **Andrew Ng Machine Learning Course** for understanding the basics. You can also check these machine learning resources.

**-Resources for Learning Machine Learning-**

**-Resources for Learning Machine Learning-**

**Step 6- ****Build Your First Machine Learning Model with scikit-learn**

**Build Your First Machine Learning Model with scikit-learn**Now, you know how to perform **data manipulation, analysis, and visualization.** It’s time to predict something and find interesting patterns from data. So start building your first Machine Learning Model.

scikit-learn is a library offered by Python.** scikit-learn** contains many useful machine learning algorithms built-in ready for you to use.

Now you need to experiment with different **machine learning algorithms**.

**Find a Machine learning problem, take data, apply different machine learning algorithms, and find out which algorithm gives more accurate results. **

**Step 7- Take Part in Data Science Competitions**

Now it’s time to practice and check your command in **Data Science.** The best way to practice is to take part in **competitions**. Competitions will make you even more **proficient in Data Science.**

When we talk about top data science competitions, Kaggle is one of the **most popular platforms** for data science. Kaggle has a lot of competitions where you can participate according to your knowledge level.

You can start with some basic level competitions such as **Titanic – Machine Learning from Disaster**, and as you gain more confidence in the competitions, you can choose more advanced competitions.

You can also check these platforms for data science competitions-

That’s all!. If you follow these steps and gain these required skills, then you can easily learn data science with Python. But the most important thing is to **keep enhancing your skills by working on more and more challenges**.

The more you practice, the more knowledge of data science you will gain. So after completing these steps, don’t stop, just **find new challenges and try to solve them**.

These projects and challenges will make your** portfolio** more impressive than others.

Now it’s time to wrap up!

**Conclusion**

In this article, I have discussed a step-by-step **Data Science with Python Roadmap.** If you have any doubts or queries, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

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