Data Analytics using Python
- Offered byDUCAT
Data Analytics using Python at DUCAT Overview
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
Data Analytics using Python at DUCAT Highlights
- Attain an industry-recognised course completion certificate
- Industry-experienced and qualified instructors
- Access updated tools, numerous applications
Data Analytics using Python at DUCAT Course details
- Analytics Training Importance
- Writing and Executing First Python Program
- Python Language Fundamentals
- Python Conditional Statements
- Looping Statements
- Standard Data Types
- String Handling
- Python List
- Python Tuple
- Python's powerful libraries and tools extract meaningful insights from data. The objective of this interdisciplinary practice is to analyze and visualize data, making informed decisions and predictions based on statistics, programming, and domain expertise.
- The goal of this course is to prepare you for a career in data-driven problem-solving and decision-making through data manipulation, visualization, and machine learning.
Data Analytics using Python at DUCAT Curriculum
Module 1: Introduction to Data Analytics
Understanding the fundamentals of Data Analytics and its applications.
Overview of Python as a tool for data analysis.
Module 2: Python Basics for Data Analytics
Learning the basics of Python programming relevant to data analysis.
Data types, variables, loops, and functions.
Module 3: Data Cleaning and Preprocessing
Techniques for cleaning and preparing data for analysis.
Handling missing values, outliers, and data transformations.
Module 4: Exploratory Data Analysis (EDA)
Visualizing and summarizing data to extract insights.
Identifying patterns, trends, and relationships.
Module 5: Statistical Analysis with Python
Applying statistical techniques for inference and hypothesis testing.
Descriptive statistics, hypothesis tests, and correlation analysis.
Module 6: Data Visualization and Presentation
Creating informative and visually appealing plots and charts.
Using libraries like Matplotlib and Seaborn.
Module 7: Predictive Modeling and Machine Learning
Building predictive models using machine learning algorithms.
Regression, classification, and model evaluation techniques.
Module 8: Time Series Analysis
Analyzing time-dependent data for forecasting and trend identification.
Techniques like moving averages and ARIMA models.
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