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Learn Python for Data Science

Do you want to enter the field of Data Science? Are you intimidated by the coding you would need to learn? Are you looking to learn Python to switch to a data science career?  

 You have come to just the right place!!!

  • Most industry experts recommend starting your Data Science journey with Python
  • Across the biggest companies and startups, Python is the most used language for Data Science and Machine Learning Projects
  • Stackoverflow survey in 2019 had Python outrank Java in the list of most loved languages

Python is a very versatile language since it has a wide array of functionalities already available. The sheer range of functionalities might sound too exhaustive and complicated, you don’t need to be well-versed with them all.

Most data scientists have a few go-to libraries for their daily tasks like:

  • For performing data cleaning and analysis – pandas
  • For basic statistical tools – numpy, scipy, scikit learn
  • For data visualization – matplotlib, seaborn

Why Python and how popular is it for Data Science?

  • Python has rapidly become the go-to language in the data science space and is among the first things recruiters search for in a data scientist’s skill set.
  • It consistently ranks top in global data science surveys and its widespread popularity will only keep on increasing in the coming years.
  • Over the years, with strong community support, this language has obtained a dedicated library for data analysis and predictive modeling.

 

Course Curriculum 

  • Statistical Analysis and Business Application
  • Learn Python from basic to advance
  • Libraries like Pandas, Numpy, Sci-kit learn, Matplotlip, NLTK
  • Learning ML algorithms with examples
  • NLP – Topic Modelling
  • Data Visualization in Python using Matplotlib
  • Introduction to Web Scrapping 
  • Introduction to Cloud Technologies azure
  • One Industry Project
  • Interview Preparations

Course curriculum for Python for Data Science

  • Data Science
  • Data Scientists
  • Examples of Data Science
  • Python for Data Science

 

  • Introduction to Data Visualization
  • Processes in Data Science
  • Data Wrangling, Data Exploration, and Model Selection
  • Exploratory Data Analysis or EDA
  • Data Visualization
  • Plotting
  • Hypothesis Building and Testing

 

  • Introduction to Statistics
  • Statistical and Non-Statistical Analysis
  • Some Common Terms Used in Statistics
  • Histogram
  • Bell Curve
  • Hypothesis Testing
  • Chi-Square Test
  • Correlation Matrix
  • Inferential Statistics
  • Introduction to Anaconda
  • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
  • Jupyter Notebook Installation
  • Jupyter Notebook Introduction
  • Basic Data Types: Integer, Float, String, None, and
  • Boolean; Typecasting
  • Creating, accessing, and slicing tuples
  • Creating, accessing, and slicing lists
  • Creating, viewing, accessing, and modifying dicts
  • Creating and using operations on sets
  • Basic Operators: ‘in’, ‘+’, ‘*’
  • Functions
  • Control Flow

 

  • NumPy Overview
  • Properties, Purpose, and Types of ndarray
  • Class and Attributes of ndarray Object
  • Basic Operations: Concept and Examples
  • Accessing Array Elements: Indexing, Slicing, Iteration,
  • Indexing with Boolean Arrays
  • Copy and Views
  • Shape Manipulation
  • Broadcasting
  • Linear Algebra
  • Introduction to Pandas
  • Data Structures
  • Series
  • DataFrame
  • Missing Values
  • Data Operations
  • Data Standardization
  • Pandas File Read and Write Support
  • SQL Operation
  • Introduction to Machine Learning
  • Machine Learning Approach
  • How Supervised and Unsupervised Learning Models Work
  • Scikit-Learn
  • Supervised Learning Models – Linear Regression
  • Supervised Learning Models: Logistic Regression
  • K Nearest Neighbors (K-NN) Model
  • Unsupervised Learning Models: Clustering
  • Unsupervised Learning Models: Dimensionality Reduction
  • Pipeline
  • Model Persistence
  • Model Evaluation – Metric Functions
  • Feature engineering

 

  • Introduction to Data Visualization
  • Python Libraries
  • Plots
  • Matplotlib Features
  • Line Properties Plot with (x, y)
  • Controlling Line Patterns and Colors
  • Set Axis, Labels, and Legend Properties
  • Alpha and Annotation
  • Multiple Plots
  • Subplots
  • Types of Plots and Seaborn
  • Mock Interview
  • Interview question
  • Resume Preparation
Download Detailed Syllabus

Common questions beginner asks about Python for Data Science?

 

1. Do I need to learn coding to learn Python?

If you are totally new to programming, no need to get intimidated by learning a whole new language.

Python is a very easy language to learn:

  • It does not have a complicated syntax and understanding Python is very intuitive.
  • You don’t need to be skilled in coding for getting started in Python.

This course is for beginners we will start right from the foundations to performing data analysis tasks in Python.

2. I am familiar with other Programming Languages like Java/C++. Will this course help me to migrate to Python?

Do you know that Python is essentially a wrapper on C? That is what makes it fast and easy to understand!

  • Though Python has recently become popular amongst Data Scientists, it was originally a general-purpose language.
  • Python is still object-oriented and follows many of the paradigms that Java does.
  • So if you are familiar with the concepts of programming, you can migrate to Python easily with this course.

3. How much Python do I need to know to enter Data Science?

  • Though Python has hundreds of libraries and many more functionalities, you don’t need to know all of them for learning Data Science
  • Rather than becoming an expert in the entire language, you would need to just be acquainted with the basic syntax of Python.
  • We will also cover the most popular libraries used by Data Scientists and which you would be using too as a future Data Scientist!

4. What if I don’t have Python installed on my system?

One of the best things about Python is the wide variety of platform that support writing it.

We will provide easy to follow instructions to work with Python using Anaconda, an extremely popular package manager platform.  No matter what Operating System you are using, we have you covered with guides for all of them.

5. What are the most popular open-source libraries that Python supports?

  • pandas, numpy, scipy, matlplotlib, seaborn are used for Data Science and Data Analysis
  • scikit-learn, tensorflow, keras are used for basic and advanced machine learning
  • libraries for deep learning like OpenCV(Computer Vision), NLTK(Natural Language Processing)

6. Will I be able to apply what I have learnt here to machine learning and data science projects?

  • The Python for Data Science course is designed to help you completely understand Python and start using it immediately for Data Science projects.
  • With regular assignments, quizzes and hands-on projects, you will be full equipped with the essential data science skillsets.