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Python For Data Analytics

"Python for Data Analytics involves using libraries like Pandas, NumPy, Matplotlib, and Scikit-learn to analyze and visualize data."

5.0 (3553 ratings)
Language : English
Validity Period : Lifetime
₹18,000 52% OFF

₹8,500/- Including 18% GST

Description Course Content How To Use

Description:

Python for Data Analytics involves using powerful libraries like Pandas, NumPy, Matplotlib, and Seaborn to manipulate, analyze, and visualize data. It enables tasks such as data cleaning, statistical analysis, and machine learning. Python’s versatility and ease of use make it ideal for handling large datasets and drawing actionable insights.

Key Highlights:

Data Manipulation: Use Pandas and NumPy for efficient data cleaning and transformation.
Visualization: Create impactful charts and graphs with Matplotlib and Seaborn.
Statistical Analysis: Perform statistical analysis using SciPy and statsmodels.
Machine Learning: Implement machine learning models with Scikit-learn.
Automation: Automate repetitive data tasks through Python scripts.
Build practical projects using Python

What you will learn:

Data Cleaning: Learn how to preprocess and clean raw data for analysis.
Data Analysis: Master techniques to explore and analyze datasets using Pandas and NumPy.
Data Visualization: Create informative and engaging visualizations with Matplotlib and Seaborn.
Statistical Methods: Understand key statistical concepts for data interpretation and decision-making.
Machine Learning: Build and evaluate predictive models using Scikit-learn.

Module 1: Introduction to Power BI (Basics)

Topic
Session-1:Overview of Business Intelligence and Power BI
Session-2: Basic Python Syntax:Variables, data types, conditionals, loops, functions.

Module 2: Data Handling with Pandas

Topic
Session-3: Basic Operation - Loading data from CSV, Excel, databases
Session-4: Basic Operation - Inspecting the dataset: df.head(), df.info(), df.describe().
Session-5: Basic Operation - Selecting and filtering data
Session-6: Basic Operation - Adding and removing columns: df['new_col'] = df['old_col'] * 2, df.drop('col', axis=1).
Session-7: Data Cleaning - Handling missing data: df.fillna(), df.dropna().
Session-8: Data Cleaning - String operations: df['column'].str.lower(), df['column'].str.replace()

Module 3: Data Manipulation

Topic
Session-9: Grouping and Aggregation: - groupby(), agg(), sum(), mean()
Session-10: Merging and Joining: - Combining data with merge(), concat().
Session-11: Pivot Tables: - Creating pivot tables

Module 4: Exploratory Data Analysis (EDA)

Topic
Session-12: Visualization with Matplotlib and Seaborn:- Basic plotting:
Session-13: Visualization with Matplotlib and Seaborn:- Scatter plots, bar charts, and histograms:
Session-14: Summary Statistics::- Mean, median, mode
Session-15: Summary Statistics::- Correlation matrix:

Module 5: Advanced Data Manipulation

Topic
Session-16: Multi-indexing: Handling multi-level indexing in dataframes.
Session-17: Reshaping Data:- Using melt() and stack() to reshape datasets.

Module 6: Feature Engineering

Topic
Session-18: Dealing with Date and Time Data:- Parsing dates: pd.to_datetime(), extracting year, month, day.
Session-19: Dealing with Date and Time Data:- Handling time series data.
Session-20: Handling Categorical Data:- Encoding categorical variables using pd.get_dummies()

Module 7: Introduction to Machine Learning

Topic
Session-21: Supervised Learning:- Linear Regression:
Session-22: Supervised Learning:- Classification with Logistic Regression, Decision Trees, etc.
Session-23: Unsupervised Learning:- K-means Clustering

Module 8: Model Evaluation

Topic
Session-24: Train/Test Split:- Splitting data into training and test sets:
Session-25: Model Metrics:- Evaluating models using accuracy, precision, recall, F1-score:

Module 9: Data Pipelines and Automation

Topic
Session-26: Building Pipelines with Sklearn:- Automating data transformation and model fitting:

Module 10: Working with Large Datasets

Topic
Session-27: Optimizing Pandas for Large Datasets:-Loading chunks, working with dask or modin for parallel processing.

End to End Practice at realtime project----120 Minutes

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