Python Data Science Training

43,866

The Data Science with Python course assists you with learning Python programming needed for Data Science. In this Data Science with Python preparing, you will dominate the method of how Python is sent for Data Science, working with Pandas library for Data Science, information cleaning, information representation, Machine Learning, progressed numeric examination, and so forth, all through certifiable ventures and contextual investigations.

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What will you learn in this Python Data Science course?

  1. Introduction to Python for Data Science
  2. OOP concepts, expressions, and functions
  3. What is SQLite in Python? Operations and classes
  4. Creating Pig and Hive UDF in Python
  5. Deploying Python for MapReduce programming
  6. Real-world Data Science projects

Who should take up this online Data Science with Python certification?

  • BI Managers and Project Managers
  • Software Developers and ETL Professionals
  • Analytics Professionals
  • Big Data Professionals
  • Those who are wanting to have a career in Python

What are the prerequisites for learning Python Data Science?

You don’t need any specific knowledge for this Data Science with Python course. Though, a basic knowledge of programming can help.

Why you should take up this Data Science with Python course?

  • Python’s design and libraries provide 10 times more productivity compared to C, C++, or Java
  • A Senior Python Developer in the United States can earn US$102,000/year – Indeed

Python is one of the best programming languages that is used for the domain of Data Science. Edutech Skills is offering the definitive Python for Data Science course for learning Python coding and running it on various systems such as Windows, Linux, and Mac, which makes it one of the highly versatile languages for the domain of Data Analytics. Upon the completion of this Data Science with Python course training, you will be able to get the best jobs in the Data Science domain at top salaries.

Module 01 – Introduction to Data Science using Python

What is Data Science, what does a data scientist do
1.2 Various examples of Data Science in the industries
1.3 How Python is deployed for Data Science applications
1.4 Various steps in Data Science process like data wrangling, data exploration and selecting the model.
1.5 Introduction to Python programming language
1.6 Important Python features, how is Python different from other programming languages
1.7 Python installation, Anaconda Python distribution for Windows, Linux and Mac
1.8 How to run a sample Python script, Python IDE working mechanism
1.9 Running some Python basic commands
1.10 Python variables, data types and keywords.

Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac

Module 02 – Python basic constructs

2.1 Introduction to a basic construct in Python
2.2 Understanding indentation like tabs and spaces
2.3 Python built-in data types
2.4 Basic operators in Python
2.5 Loop and control statements like break, if, for, continue, else, range() and more.

Hands-on Exercise –
1.Write your first Python program
2. Write a Python function (with and without parameters)
3. Use Lambda expression
4. Write a class
5. Create a member function and a variable
6. Create an object and write a for loop to print all odd numbers

Module 03 – Math’s for DS-Statistics & Probability

3.1 Central Tendency
3.2 Variability
3.3 Hypothesis Testing
3.4 Anova
3.5 Correlation
3.6 Regression
3.7 Probability Definitions and Notation
3.8 Joint Probabilities
3.9 The Sum Rule, Conditional Probability, and the Product Rule
3.10 Bayes Theorem

Hands-on Exercise –

1. We will analyze both categorical data and quantitative data
2. Focusing on specific case studies to help solidify the week’s statistical concepts

Module 04 – OOPs in Python (Self paced)

4.1 Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
4.2 What are access modifiers, instances, class members
4.3 Classes and objects
4.4 Function parameter and return type functions
4.5 Lambda expressions.

Hands-on Exercise –
1. Writing a Python program and incorporating the OOP concepts

Module 05 – NumPy for mathematical computing

5.1 Introduction to mathematical computing in Python
5.2 What are arrays and matrices, array indexing, array math, Inspecting a NumPy array, NumPy array manipulation

Hands-on Exercise –

1. How to import NumPy module
2. Creating array using ND-array
3. Calculating standard deviation on array of numbers and calculating correlation between two variables.

Module 06 – SciPy for scientific computing

6.1 Introduction to SciPy, building on top of NumPy
6.2 What are the characteristics of SciPy
6.3 Various sub-packages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes Theorem with SciPy.

Hands-on Exercise:

1. Importing of SciPy
2. Applying the Bayes theorem on the given dataset.

Module 07 – Data manipulation

7.1 What is a data Manipulation. Using Pandas library
7.2 NumPy dependency of Pandas library
7.3 Series object in pandas
7.4 DataFrame in Pandas
7.5 Loading and handling data with Pandas
7.6 How to merge data objects
7.7 Concatenation and various types of joins on data objects, exploring dataset

Hands-on Exercise –

1. Doing data manipulation with Pandas by handling tabular datasets that includes variable types like float, integer, double and others.
2. Cleaning dataset, Manipulating dataset, Visualizing dataset

Module 08 – Data visualization with Matplotlib

8.1 Introduction to Matplotlib
8.2 Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more
8.3 Matplotlib API

Hands-on Exercise –
1. Deploying Matplotlib for creating pie, scatter, line and histogram.
2. Subplots and Pandas built-in data visualization.

Module 09 – Machine Learning using Python

9.1 Revision of topics in Python (Pandas, Matplotlib, NumPy, scikit-Learn)
9.2 Introduction to machine learning
9.3 Need of Machine learning
9.4 Types of machine learning and workflow of Machine Learning
9.5 Uses Cases in Machine Learning, its various algorithms
9.6 What is supervised learning
9.7 What is Unsupervised Learning

Hands-on Exercise –

1. Demo on ML algorithms

Module 10 – Supervised learning

10.1 What is linear regression
10.2 Step by step calculation of Linear Regression
10.3 Linear regression in Python
10.4 Logistic Regression
10.5 What is classification
10.6 Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self paced), Support Vector Machine(self paced), XGBoost (self paced)

Hands-on Exercise – Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning.

Module 11 – Unsupervised Learning

11.1 Introduction to unsupervised learning
11.2 Use cases of unsupervised learning
11.3 What is clustering
11.4 Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering(self-paced)
11.5 What is K-means clustering
11.6 Step by step calculation of k-means algorithm
11.7 Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule mining(self-paced)-support, confidence, lift
11.8 Apriori Algorithm

Hands-on Exercise –
1. Setting up the Jupyter notebook environment
2. Loading of a dataset in Jupyter
3. Algorithms in Scikit-Learn package for performing Machine Learning techniques and training a model to search a grid.
4. Practice on k-means using Scikit
5. Practice on Apriori

Module 12 – Python integration with Spark (Self paced)

12.1 Introduction to PySpark
12.2 Who uses PySpark, need of spark with python
12.3 PySpark installation
12.4 PySpark fundamentals
12.5 Advantage over MapReduce, PySpark
12.6 Use-cases PySpark and demo.

Hands-on Exercise:
1. Demonstrating Loops and Conditional Statements
2. Tuple – related operations, properties, list, etc.
3. List – operations, related properties
4. Set – properties, associated operations, dictionary – operations, related properties.

Module 13 – Dimensionality Reduction

13.1 Introduction to Dimensionality
13.2 Why Dimensionality Reduction
13.3 PCA
13.4 Factor Analysis
13.5 LDA

Hands-on Exercise –
Practice Dimensionality reduction Techniques : PCA, Factor Analysis, t-SNE, Random Forest, Forward and Backward feature

Module 14 – Time Series Forecasting

14.1 White Noise
14.2 AR model
14.3 MA model
14.4 ARMA model
14.5 ARIMA model
14.6 Stationarity
14.7 ACF & PACF

Hands-on Exercise –
1. Create AR model
2. Create MA model
3. Create ARMA model