Data Science with Python

The Data Science with Python course is a comprehensive program designed for learners to gain skills in the field of data science and analytics using Python. It covers a wide range of topics from the basics of data science, machine learning, and statistical analysis to advanced topics such as Natural Language Processing and deep learning. The course is structured in a modular fashion, starting with an introduction to data science, and progressively moving through Python programming essentials, Data Manipulation with Pandas, data visualization with Matplotlib and Seaborn, and machine learning with Scikit-Learn.

Retail Price: $2,595.00

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Course Days: 5


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Learning Objectives and Outcomes

  • Understand the fundamentals of data science and its significance in today's data-driven world.
  • Acquire proficiency in Python for data science, including setting up the environment, understanding data types, and applying control statements.
  • Learn the essentials of machine learning, including supervised, unsupervised learning, and the main challenges in the field.
  • Develop skills in data analytics, including exploratory data analysis (EDA), data analytics processes, and communication of findings.
  • Gain expertise in statistical analysis and business applications, focusing on statistical processes, data distribution, and inferential statistics.
  • Master data manipulation using Pandas for structured data operations and data cleaning techniques for preparing datasets.
  • Get hands-on experience with scientific computing libraries such as NumPy for mathematical operations and SciPy for advanced scientific calculations.
  • Explore data visualization using Matplotlib and Seaborn to communicate data insights effectively through various chart types.
  • Delve into natural language processing (NLP) with Scikit Learn, covering NLP overview, applications, and model training.
  • Understand feature engineering and selection techniques to improve machine learning model performance and learn about key performance metrics and parameter tuning.

Course Prerequisites

  • Basic understanding of computer programming principles.
  • Familiarity with the Python programming language, including variables, control structures, functions, and data types.
  • Knowledge of basic mathematical concepts, especially algebra and statistics.
  • An understanding of fundamental data structures like lists, sets, dictionaries, and tuples in Python.
  • Basic problem-solving skills and logical thinking.
  • Willingness to learn and explore new concepts in data analysis and machine learning.
  • Ability to install software and manage files on your personal computer.

These prerequisites are designed to ensure that learners can comfortably grasp the course content and participate actively in the learning process. No advanced knowledge is required, and the course aims to build upon these foundational skills to help learners become proficient in data science using Python.

Target Audience for Data Science with Python

This course is designed for professionals seeking to harness data for insightful decision-making and predictive analytics.

  • Aspiring Data Scientists
  • Data Analysts
  • Software Engineers looking to transition into Data Science roles
  • Statisticians planning to leverage Python for data analysis
  • Business Analysts wanting to understand data science techniques
  • Machine Learning Enthusiasts
  • IT Professionals interested in analytics and big data
  • Graduates seeking a career in data science and machine learning
  • Researchers requiring data analysis skills using Python
  • Project Managers overseeing data-driven projects
  • Entrepreneurs who need to grasp data science fundamentals for their ventures
  • Marketing Professionals looking to use data for better market understanding
  • Finance Professionals aiming to apply data science in financial analysis and forecasting
  • Product Managers aiming to base their strategies on data-driven insights
  • BI (Business Intelligence) and Data Warehousing Professionals
  • Quality Analysts wanting to understand data science processes
  • Any professional or student with a keen interest in data science, machine learning, and Python programming

Outline

Lesson 00 - Course Overview
Course Overview

Lesson 01 - Data Science Overview
? Introduction to Data Science
? Different Sectors Using Data Science
? Purpose and Components of Python

Lesson 02 - Data Analytics Overview
? Data Analytics Process
? Knowledge Check
? Exploratory Data Analysis (EDA)
? EDA-Quantitative Technique
? EDA - Graphical Technique
? Data Analytics Conclusion or Predictions
? Data Analytics Communication
? Data Types for Plotting
? Data Types and Plotting

Lesson 03 - Statistical Analysis and Business Applications
? Introduction to Statistics
? Statistical and Non-statistical Analysis
? Major Categories of Statistics
? Statistical Analysis Considerations
? Population and Sample
? Statistical Analysis Process
? Data Distribution
? Dispersion
? Histogram
? Testing
? Correlation and Inferential Statistics

Lesson 04 - Python Environment Setup and Essentials
? Anaconda
? Installation of Anaconda Python Distribution (contd.)
? Data Types with Python
? Basic Operators and Functions

Lesson 05 - Mathematical Computing with Python (NumPy)
? Introduction to Numpy
? Activity-Sequence it Right
? Demo 01-Creating and Printing an ndarray
? Knowledge Check
? Class and Attributes of ndarray
? Basic Operations
? Activity-Slice It
? Copy and Views
? Mathematical Functions of Numpy

Lesson 06 - Scientific computing with Python (Scipy)
? Introduction to SciPy
? SciPy Sub Package - Integration and Optimization
? Knowledge Check
? SciPy sub package
? Demo - Calculate Eigenvalues and Eigenvector
? Knowledge Check
? SciPy Sub Package - Statistics, Weave and IO

Lesson 07 - Data Manipulation with Pandas
? Introduction to Pandas
? Knowledge Check
? Understanding DataFrame
? View and Select Data Demo
? Missing Values
? Data Operations
? Knowledge Check
? File Read and Write Support
? Knowledge Check-Sequence it Right
? Pandas Sql Operation

Lesson 08 - Machine Learning with Scikit–Learn
? Machine Learning Approach
? Understand data sets and extract its features
? Identifying problem type and learning model
? How it Works
? Train, test and optimizing the model
? Supervised Learning Model Considerations
? Knowledge Check
? Scikit-Learn
? Knowledge Check
? Supervised Learning Models - Linear Regression
? Supervised Learning Models - Logistic Regression
? Unsupervised Learning Models
? Pipeline
? Model Persistence and Evaluation

Lesson 09 - Natural Language Processing with Scikit Lear
? NLP Overview
? NLP Applications
? Knowledge Check
? NLP Libraries-Scikit
? Extraction Considerations
? Scikit Learn-Model Training and Grid Search

Lesson 10 - Data Visualization in Python using matplotlib
? Introduction to Data Visualization
? Knowledge Check
? Line Properties
? (x,y) Plot and Subplots
? Knowledge Check
? Types of Plots

Lesson 11 - Web Scraping with BeautifulSoup
? Web Scraping and Parsing
? Knowledge Check
? Understanding and Searching the Tree
? Navigating options
? Demo 3 Navigating a Tree
? Knowledge Check
? Modifying the Tree
? Parsing and Printing the Document

Lesson 12 - Python integration with Hadoop MapReduce and Spark
? Why Big Data Solutions are Provided for Python
? Hadoop Core Components
? Python Integration with HDFS using Hadoop Streaming
? Demos
? Knowledge Check
? Python Integration with Spark using PySpark



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