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What Every Beginner Must Know About Using Python for Data Science

February 23, 2021 | Data Science

So, you’ve decided to become a data scientist or maybe looking forward to expanding your tool repository. Well, you’ve landed on the right page. The aim here is to provide a learning path to the people who want to use Python for data science and are new to it. But before exploring the things that you must know or learn to use Python in data science, we should briefly answer why you should learn Python in the first place.

Python is one of the most valuable programming languages needed in data science. Not only is the language popular and has a great community but it also is easy to read, learn, and work with. According to a report by Gregory Piatetsky, around 66% of data scientists use Python daily, making it the most sought-after language for analytics professionals.

Data science experts expect this upward trend to continue with booming development in the Python ecosystem. And even though you are just a beginner in your journey to learn Python, you’d be glad to know that the opportunities are abundant. According to Indeed, the average salary for a Data Scientist is around $122,835 and this number is only expected to increase. You can see how getting a data science with Python training will help you in a long run.

Without further ado, let’s throw light at what you need to know to use Python in data science, and bring your career to a new level.

The Basic Data Structures

Data structures are described as a method of organizing and storing the data in an easily accessible and modifiable way. Dictionaries, Lists, Sets, Strings, Tuples are some of the built-in data structures. All these have their advantages and disadvantages, so you need to know where to use them to get the best possible results.

You need to learn how to break the data down to a form that can be easily worked with, which can be achieved with Python Data Cleaning with NumPy and Pandas.

Do not blindly install Python for data science. You must know what tools you’ll be needing to engage in data analytics. The best approach to go around this is the Anaconda-Python Distribution.

Jupyter/iPython Notebook

Jupyter Notebook is an interactive programming environment that allows for coding, data exploration, and debugging in the web browser. You can mix code, graphics, and text. It is pre-installed with Anaconda which means you can use it as soon as it is installed.

Python Libraries

There are several active data science and machine learning libraries in Python that can be leveraged using Python for data science.

Here are some of the Python libraries to use in data science –

  • Matplotlib: Useful for data visualization.
  • NumPy: Helps with fast, precompiled functions for numerical routines.
  • SciPy: Useful for linear algebra, integration, statistics, optimization, and other tasks.
  • Pandas: Used for data wrangling and munging.
  • PyTorch: For natural language processing and deep learning.
  • Seaborn: For visualization of statistical models.
  • Scikit-Learn: Useful in implementing algorithms on data sets.
  • PySpark: For leveraging Apache Spark and Python to interface with datasets.
  • TensorFlow: For dataflow programming across various tasks.

Data Science

Here’s how you can kick-start your journey –

  • Get enrolled in online data science with Python course to learn the fundamentals of the Python language, how to incorporate the language into data science, and a variety of other specializations of Python.
  • Practice getting hands-on experience, by building mini Python projects including Python and Pandas basics, user survey data analysis, and other guided projects. Read guidebooks, blogs, open-source codes to learn the best practices in Python and data science.
  • Build a data science portfolio while learning Python which involves a data cleaning project, data visualization project, and a machine learning project.
  • Sharpen your skills by applying advanced data science techniques after completing the data science with Python certification. You’ll apply regression, classification, clustering models, bootstrapping models, and creating neural networks. At this stage, you’ll be able to create models using live data feeds.
Where to Learn Python for Data Science?

There are tons of Python learning courses online, but if you’re looking to learn python for data science, Cognixia has you covered. Get data science with Python certification through a comprehensive and hands-on approach.

This online data science with Python course provides you with the opportunity to experiment with a wide variety of Data Science and ML algorithms.

This Data Science and Python course will cover –

  • Introduction to Data Science, Data Science Project Life Cycle & Python Programming
  • Basic statistics
  • Probability theory
  • Statistical analysis using Pandas and Matplotlib
  • Inferential statistics
  • Applied inferential statistics
  • Machine learning concepts (with case studies)
  • Hands-on lab experience of each course!
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