Today’s economy is more focused on analysis – companies have been collecting data for several years. Also, there is a massive demand for people who can dig in and interpret the data. They are known as a data scientist.

If you are looking for a roadmap to start your career in data science, here is a guide for you. In this guide, we will discuss how to start your career in data science with Python. Let’s get started!

How Much Python Do You Need to Learn for Data Science?

If you are an aspiring data scientist, you must be wondering how much Python is required to work on data science. And if you have read Introduction to Python, you already know that because of its efficiency and code readability, everyone knows that Python is one of the most widely used programming languages. As a result, Python is often the choice of data scientists to perform data analysis using web applications or production environments.

How Much Is Time Required to Learn Python for Data Science?

Most aspiring data scientists or analysts want to know: How long does it take to learn Python for data science?

There are several assessments on this issue. For data science, evaluation varies from 3 months to a year, as long as it is practiced consistently. Of course, it also depends on how much time you can spend studying Python for Data Science. But most of the students need at least three months to complete the Python Data Science study path.

Roadmap to start your career in Python for Data Science

The availability of NumPy, Pandas, Matplotlib, SciPy, etc., makes anyone with an essential programming background build a machine learning model. So, for making a career in data science, you should be familiar with Python fundamentals and the standard libraries.

Python Standard Libraries to build Your Career In Data Science

The standard Python library contains a wide range of modules for daily programming and is included in the standard version of Python, meaning no additional installation is required. It provides modules for functions such as interacting with the operating system, reading and writing CSV files, generating random numbers, and working with dates and times. As a data geek, you need to know about the following vital libraries that make Python a robust and powerful tool for analyzing and visualizing data.

Pandas

Pandas is an open-source package in Python and is a must in data science. Moreover, Pandas is the most widely used and popular data science library. It is designed for practical data analysis in finance, social science, statistics, and engineering. Pandas help with high-performance and easy-to-use data structures and tools for analyzing labeled data. It works well with incomplete, messy, and unmarked data and provides tools for formatting, merging, resizing, and cutting data sets.

NumPy

NumPy is the most fundamental library for Python developers. In addition, NumPy handles large datasets effectively. However, as data scientists or aspiring data science professionals, we need to have a solid grasp of NumPy and how it works in Python.

NumPy stands for Numerical Python and is one of the most valuable scientific libraries in Python programming. It provides a multidimensional array for large objects and support for various tools to work with them. Many other libraries such as Pandas,  Matplotlib, and Scikit-learn are built on top of this amazing library.

Matplotlib

Matplotlib is a cross-platform library for data visualization and graphics for Python and its numerical extension NumPy. As such, it provides a viable open-source alternative to MATLAB. Developers can also use the matplotlib API (Application Programming Interface) to embed graphics in the application GUI.

You can generate graphs, histograms, power spectra, bar charts, error diagrams, scattering graphs, etc., with a few lines of code.

SciPy

Scientific Python or SciPy is a free, open-source data science library. It is used for high-level technical calculations. It is based on NumPy and uses arrays as the primary data structure. In addition, it provides high-level commands for data manipulation and data visualization.

5 Quick Steps to Start Your Career In Data Science

Here are quick 5-steps to start your career in data science.

Step 1. Warming up

Now that you’ve decided, it’s time to set up your machine. The easiest way to continue is to download Anaconda from Continuum.io. It is packed with most of the things you will ever need. The main disadvantage of this route is that you have to wait for Continuum to update their packages even if an update is available for the core libraries. However, if you are a beginner, it hardly matters.

Step 2. Learn the Python from Scratch

You have to start by understanding the basics of the language, libraries, and data structure. Vidya’s free Analytics course for Moreover, Python is the best place to start your data science journey.

Step 3. Learn Regular Expressions in Python

You’ll need to use them a lot to clean up the data, especially if you’re working on textual data. The fastest way to learn regular expressions is to look at Google Classes.

Step 4. Learn Scientific Libraries in Python like NumPy, SciPy, Matplotlib, and Pandas

This is where the fun begins! Here is a brief introduction to the various libraries. So let’s start practicing some common operations.

  • Practice NumPy lessons carefully, especially NumPy arrays. This will set a good foundation for future things.
  • Then take a look at the SciPy tutorial. Check out the introduction and basics and do the rest according to what you need.
  • Finally, let’s look at pandas. Pandas provide data frame functionality for Python (like R). Here too you have to spend a lot of time in training. Pandas will become the most effective tool for analyzing all averaged data. Start with a short 10-minute introduction to Pandas. Then move on to the more detailed lesson on Pandas.

Step 5. Practice!

You already have everything you need for technical skills in Python Programming. Then it’s a matter of practice. So go on, immerse yourself in one of the live competitions currently taking place online and try everything you’ve learned. 

Learn scikit-learn and machine learning. You can review machine learning for effective data visualization, controlled learning algorithms such as regression, decision trees, ensemble modeling, and unsupervised learning algorithms such as clustering.

In a nutshell, it entirely depends upon your practice. At techlearn, we are preparing for an excellent session about starting your career in data science. So feel free to join us. Book Now!

You May Also Like: How to build your Data Science portfolio?

LEAVE A REPLY

Please enter your comment!
Please enter your name here