Basic Data Analytics Training Using Python

 

Training Object

This training is designed to provide beginners with a solid foundation in data analytics using Python. The course will cover essential concepts, tools, and techniques that will help you analyze and visualize data effectively, making use of Python's powerful libraries.

Introduction to Data Analytics and Python

• Understanding Data Analytics
• Introduction to Python for Data Analysis
• Setting up Python Environment (Installing Anaconda, Jupyter Notebooks)

Python Basics for Data Analysis

• Data Types (Strings, Integers, Floats, Lists, Tuples, Dictionaries)
• Variables and Basic Operations
• Control Flow (If-Else, Loops)
• Functions and Modules

Working with Libraries

• NumPy: Introduction to arrays, mathematical operations, and working with large datasets
• Pandas: DataFrames, Series, data cleaning, handling missing data
• Matplotlib/Seaborn: Introduction to data visualization, creating basic charts (line, bar, scatter)

Data Import and Export

• Importing data from CSV, Excel, and other file formats
• Exporting data to CSV, Excel
• Connecting to databases using Python

Data Cleaning and Preprocessing

• Identifying and handling missing values
• Filtering and transforming data
• Normalization and standardization of data
• Handling outliers

Data Exploration and Analysis

• Descriptive statistics: Mean, Median, Mode, Variance, Standard Deviation
• Grouping and aggregation of data
• Correlation analysis

Data Visualization

• Creating basic plots (histograms, boxplots, pie charts, bar charts)
• Customizing plots (titles, labels, legends)
• Advanced visualization (heatmaps, pair plots)

Basic Machine Learning Concepts (Optional)

• Introduction to machine learning with scikit-learn
• Basic regression and classification models
• Model evaluation techniques (accuracy, confusion matrix)

Study Project Work

• Real-world data analysis project
• Data collection, cleaning, visualization, and interpretation
• Presenting findings and insights

Expected Learning Outcomes

• Gain proficiency in Python for data analysis.
• Learn how to manipulate, clean, and analyze data using libraries like NumPy and Pandas.
• Visualize data trends and patterns using Matplotlib and Seaborn.
• Develop a basic understanding of machine learning algorithms.
• Be equipped to handle real-world data problems and projects.