Data Analytics with Python

Your Path to Becoming a Data Analytics Pro
Why This Training?
Data is the currency of the modern world, and Python has emerged as the lingua franca for data analytics. Whether you're new to the field or looking to enhance your skills, this course is tailored to impart the essence of data analytics using Python – one of the most versatile and widely-used languages in the world.
Duration: 30 Hours (online / virtual live session)

Who Should Attend? 

Aspiring data scientists, data analysts, Python enthusiasts, and anyone intrigued by the realm of data and insights.

Course Breakdown

 Diving Into Python & Its Data Powerhouse: Witness Python's evolution and why it's the go-to language for data enthusiasts.
 Setting the Stage: From installation to Jupyter Notebook setup, ensure you're well-equipped for the journey.
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 Python 101 for Analysts: Brush up on foundational Python concepts and discover the art of writing Pythonic code.
 Mastering Pandas: Grasp the capabilities of Pandas for data wrangling, cleaning, and transformation.
 Art of Visualization: Explore the world of Matplotlib & Seaborn, crafting compelling visuals that narrate data stories.
 Diving Deeper: Unravel advanced analytics techniques from time series analysis to a primer on machine learning.
 Coding Like a Pro: Unearth best practices, optimization techniques, and the magic of debugging in Python.
 Bringing It All Together: Conclude with a real-world case study, applying your newfound knowledge, followed by an interactive Q&A.


 Basic Programming Knowledge: Familiarity with any programming language is beneficial.
 Zeal to Learn: An inquisitive mind ready to explore the vast world of data.

Training Materials Needed

With Python (version 3.x) installed.
Internet Connection
For downloading necessary libraries and datasets.
Jupyter Notebook
For interactive data analysis (instruction on setup provided in the course).
Text Editor
Any text editor for scripting (e.g., Visual Studio Code, Atom).
Sample Datasets
To be provided by the trainers or downloadable via provided links.
Python Libraries
Pandas, Matplotlib, Seaborn, and a few others. Instructions on installation will be given.
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Training Content

Data Analytics with Python

1. Introduction to Python & Data Analytics

Objective: Set the context and highlight the significance of Python in data analytics.
  • Brief history and rise of Python in data analytics.
  • Advantages of Python: Libraries, community, versatility.

2. Setting up the Python Environment

Objective: Ensure participants have the necessary tools installed and ready.
2.1. Installing Python and Pip
  • Python versions and compatibility.
2.2. Virtual Environments
  • Why use virtual environments? Introduction to venv and conda.
2.3. Jupyter Notebook & Lab
  • Setting up and basics of Jupyter for interactive data analysis.

3. Python Basics for Data Analytics

Objective: Refresh or introduce Python's foundational concepts.
3.1. Data Types and Variables
  • Strings, numbers, lists, dictionaries, tuples.
3.2. Control Structures
  • Loops, conditionals, and functions.
3.3. Pythonic Code & List Comprehensions
  • Writing efficient and readable Python code.

4. Data Manipulation with Pandas

Objective: Delve into the primary library for data analysis in Python.
4.1. Introduction to Pandas
  • DataFrames and Series.
4.2. Reading and Writing Data
  • CSV, Excel, SQL databases.
4.3. Data Cleaning and Transformation
  • Handling missing values, merging datasets, filtering, and aggregation.

5. Data Visualization with Matplotlib and Seaborn

Objective: Visualize data effectively to gain insights.
5.1. Basics of Matplotlib
  • Line plots, scatter plots, bar charts, histograms.
5.2. Advanced Visualization with Seaborn
  • Heatmaps, pair plots, and more.
5.3. Customizing and Storing Visuals
  • Modifying axes, titles, legends, and saving plots.

6. Advanced Data Analytics Techniques

Objective: Introduce advanced analytical techniques and tools in Python.
6.1. Time Series Analysis
  • Handling dates, time resampling, and rolling statistics.
6.2. Text Analytics
  • Basics of text processing using libraries like NLTK or TextBlob.
6.3. Basic Introduction to Machine Learning
  • Train/test split, basic regression and classification using scikit-learn.

7. Best Practices and Debugging

Objective: Offer insights on effective coding and problem-solving in Python.
7.1. Writing Efficient Code
  • Code profiling and optimization techniques.
7.2. Debugging Common Errors
  • Using IDEs or tools like pdb for debugging.

8. Conclusion, Case Study & Q&A Session

Objective: Apply learned concepts in a real-world scenario and address queries.
  • Guided case study: A hands-on project encapsulating the training's core concepts.
  • Recap and highlight avenues for further exploration in Python-based data analytics.
  • Engaging in Q&A.
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