Deep Data Science For Data Scientists End to End TM

1. Introduction to Data Science

Overview:-

This section provides a broad introduction to Data Science, its significance, and its impact across various industries.

Topics Covered:-

What is Data Science?

The Importance of Data in the Modern World

Applications of Data Science in Various Domains

Key Roles in Data Science: Data Scientists, Data Engineers, and Data Analysts

Learning Outcomes:-

Understand the basics of Data Science and its relevance.

Identify key roles and applications of Data Science.

2. Data Science Session Part 2

Overview:-

A continuation of the introduction, diving deeper into the tools and technologies used in Data Science.

Topics Covered:-

Overview of Data Science Tools and Technologies

Introduction to Data Wrangling, Exploration, and Visualization

Understanding Big Data and its Challenges

Real-World Case Studies in Data Science

Learning Outcomes:-

Gain insight into the tools and techniques used in Data Science.

Analyze real-world examples of Data Science in action.

3. Data Science Vs Traditional Analysis

Overview:-

This section contrasts Data Science with traditional data analysis techniques, highlighting the differences and advancements.

Topics Covered:-

Traditional Data Analysis Techniques

Evolution from Traditional Analysis to Data Science

Differences in Methodology, Tools, and Outcomes

Advantages of Data Science over Traditional Analysis

Learning Outcomes:-

Understand the evolution from traditional analysis to Data Science.

Identify the key differences and advantages of Data Science.

4. Data Scientist Part 1

Overview:-

Introduction to the role of a Data Scientist, including skills, tools, and responsibilities.

Topics Covered:-

Who is a Data Scientist?

Essential Skills for Data Scientists: Programming, Statistics, and Domain Knowledge

Common Tools Used by Data Scientists

The Day-to-Day Responsibilities of a Data Scientist

Learning Outcomes:-

Understand the role and importance of a Data Scientist.

Identify the essential skills and tools required for a Data Scientist.

5. Data Scientist Part 2

Overview:-

A deeper dive into the practical aspects of being a Data Scientist, including challenges and career opportunities.

Topics Covered:-

Challenges Faced by Data Scientists

Ethical Considerations in Data Science

Career Pathways and Growth Opportunities for Data Scientists

Building a Portfolio and Gaining Practical Experience

Learning Outcomes:-

Gain insight into the challenges and ethical considerations in Data Science.

Learn about career opportunities and how to build a strong Data Science portfolio.

6. Data Science Process Overview

Overview:-

An overview of the Data Science process, including stages and methodologies.

Topics Covered:-

The Data Science Lifecycle

Understanding Data Collection and Data Cleaning

Exploratory Data Analysis (EDA)

Model Building and Evaluation

Deployment and Monitoring of Data Science Models

Learning Outcomes:-

Understand the stages involved in the Data Science process.

Gain a high-level understanding of each step from data collection to model deployment.

7. Data Science Process Overview Part 2

Overview:-

Continuation of the Data Science process, focusing on more advanced concepts and tools.

Topics Covered:-

Advanced Data Cleaning Techniques

Feature Engineering and Selection

Hyperparameter Tuning and Model Optimization

Introduction to Model Interpretability

Post-Deployment Monitoring and Maintenance

Learning Outcomes:-

Master advanced techniques in the Data Science process.

Learn how to optimize models and ensure they remain effective post-deployment.

8. Introduction to Python for Data Science

Overview:-

This section introduces Python as a primary tool for Data Science.

Topics Covered:-

Why Python for Data Science?

Setting Up the Python Environment for Data Science

Introduction to Jupyter Notebooks

Basic Python Syntax and Operations

Overview of Python Libraries for Data Science

Learning Outcomes:-

Set up and use Python for Data Science tasks.

Write basic Python code and use Jupyter Notebooks.

9. Python Libraries for Data Science

Overview:-

A focused introduction to essential Python libraries used in Data Science.

Topics Covered:

Overview of NumPy, Pandas, Matplotlib, and Seaborn

Working with NumPy Arrays

Data Manipulation with Pandas

Data Visualization with Matplotlib and Seaborn

Introduction to SciPy and Scikit-Learn for Machine Learning

Learning Outcomes:-

Gain proficiency in using essential Python libraries for data manipulation and visualization.

Prepare data for analysis and build simple visualizations.

10. Introduction to R for Data Science

Overview:-

Introduces R as an alternative tool for Data Science, focusing on its strengths and ecosystem.

Topics Covered:-

Why R for Data Science?

Setting Up the R Environment

Basic R Syntax and Operations

Introduction to RStudio

Overview of R Libraries for Data Science (e.g., dplyr, ggplot2)

Learning Outcomes:-

Set up and use R for Data Science tasks.

Write basic R code and use RStudio effectively.