Data Science With Python Course Syllabus

Introduction

If you are interested in a career in data science, taking our data science with python course in Chennai would be the best move. Our expert trainers formulated an easy-to-learn data science with python course syllabus that would help you in getting started.

With the help of our data science with python course syllabus, you will not only learn basic concepts and principles of data science, but become an expert data scientist as well. Our data science with python course syllabus will make you proficient in python libraries like Plotly, Seaborn, Matplotlib etc and help you create insightful data visualizations.

We offer an all-inclusive data science with python course syllabus that covers all the important aspects of data science, from python fundamentals to advanced machine learning and artificial intelligence techniques, linear algebra, statistics, business intelligence and data engineering concepts.

By learning our data science with python course syllabus, you can gain the knowledge and skills required to thrive in your chosen career path. Visit https://www.ficusoft.in/ to know more.

Data Science With Python Course Syllabus

Module 1 – Data Science Overview

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

Module 2 – Data Analytics Overview

• 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 

Module 3 – Git & GitHib

• Setting up Your GitHub Account
• Configuring Your First Git Repository
• Making Your First Git Commit
• Pushing Your First Commit to GitHub
• Git and GitHub Workflow Step-by-Step

Module 4 – Introduction to Sql and Databases

• SQL/RDBMS database management
• SQL Queries
• CRUD Operations

Module 5 – Statistical Analysis and Business Applications

• Introduction to Statistics
• Statistical and Non-Statistical Analysis
• Some Common Terms Used in Statistics
• Data Distribution: Central Tendency, Percentiles, Dispersion
• Histogram
• Bell Curve
• Hypothesis Testing
• Chi-Square Test
• Correlation Matrix
• Inferential Statistics

Module 6 – Python: Environment Setup and Essentials

• Introduction to Anaconda
• Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
• Jupyter Notebook Installation
• Jupyter Notebook Introduction
• Variable Assignment
• 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

Module 7 – Mathematical Computing with Python (NumPy)

• 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
• Universal Functions (ufunc)
• Shape Manipulation
• Broadcasting
• Linear Algebra

Module 8 – Scientific computing with Python (Scipy)

• SciPy and its Characteristics
• SciPy sub-packages
• SciPy sub-packages –Integration
• SciPy sub-packages – Optimize
• Linear Algebra
• SciPy sub-packages – Statistics
• SciPy sub-packages – Weave
• SciPy sub-packages – I O

Module 9 – Data Manipulation with Python

•         Introduction to Pandas

• Data Structures
• Series
• DataFrame
• Missing Values
• Data Operations
• Data Standardization
• Pandas File Read and Write Support
• SQL Operation

Module 10 – Machine Learning with Python (Scikit–Learn)

• 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

Module 11 – Descriptive Statistics – Data understanding

• Observations, variables, and data matrices
• Types of variables
• Measures of Central Tendency
• Arithmetic Mean / Average
• Merits & Demerits of Arithmetic Mean and Mode
• Merits & Demerits of Mode and Median
• Merits & Demerits of Median Variance

Module 12 – Probability Basics

• Notation and Terminology
• Unions and Intersections
• Conditional Probability and Independence

Module 13 – Probability Distributions

• Random Variable
• Probability Distributions
• Probability Mass Function
• Parameters vs. Statistics
• Binomial Distribution
• Poisson Distribution
• Normal Distribution
• Standard Normal Distribution
• Central Limit Theorem
• Cumulative Distribution function

Module 14 – Natural Language Processing with Scikit-Learn

• NLP Overview
• NLP Approach for Text Data
• NLP Environment Setup
• NLP Sentence analysis
• NLP Applications
• Major NLP Libraries
• Scikit-Learn Approach
• Scikit – Learn Approach Built – in Modules
• Scikit – Learn Approach Feature Extraction
• Bag of Words
• Extraction Considerations
• Scikit – Learn Approach Model Training
• Scikit – Learn Grid Search and Multiple Parameters
• Pipeline

Module 15 – Data Visualization in Python using Matplotlib

• 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

Module 16 – Data Science with Python Web Scraping

• Web Scraping
• Common Data/Page Formats on The Web
• The Parser
• Importance of Objects
• Understanding the Tree
• Searching the Tree
• Navigating options
• Modifying the Tree
• Parsing Only Part of the Document
• Printing and Formatting
• Encoding

Module 17 – Python integration with Hadoop, MapReduce and Spark

• Need for Integrating Python with Hadoop
• Big Data Hadoop Architecture
• MapReduce
• Cloudera QuickStart VM Set Up
• Apache Spark
• Resilient Distributed Systems (RDD)
• PySpark
• Spark Tools

Module 18 – Life cycle of Data Science and Machine Learning Projects

• What is Project Pipeline?
• Data Collection
• Data Preprocessing
• Model Building
• Training and Testing
• Model Evaluation
• Model Deployment

 Conclusion

Our seasoned trainers have hands-on experience in the field and provide you with an excellent data science with python course syllabus that would guide you through the complexities of data science with python, helping you build a strong foundation.

Our data science with python course syllabus comes with many hands-on projects that would help you apply your theoretical knowledge and learn more about data science concepts. You can take our data science with python training in Chennai either online or offline and secure a strong spot in the top MNCs.

Our data science with python course syllabus will definitely pave your way to career development in this vibrant and rewarding area. Catch our training programs and webinars live through https://www.facebook.com/ficusofttechnologies/.