About Data Science with Python Course
The program builds a foundation in Data Science with Python by training you on industry standard tools and techniques through a practical, industry oriented curriculum. This program requires no prior knowledge of coding in Python, R or SQL and begins from fundamentals. By the end of the program the candidates have a deep understanding of statistical techniques critical to Data Analysis and they are able to create Analytical models using real life data to drive business impact.
Data analysis is the methodology of gathering data and processing it, in order to get useful insights. Data Analyst is all about the utilization of the major techniques, related to data visualization and manipulation. The techniques are used to expose the most valuable insights. These insights allow the companies to formulate better strategies and to make better decisions.
What you will learn
- Understand Python language basics and how they apply to data science.
- Practice iterative data science using Jupyter notebooks.
- Analyze data using Python libraries like pandas and numpy.
- Create stunning data visualizations with matplotlib and seaborn.
- Build machine learning models using scipy and scikitlearn.
- Demonstrate proficiency in solving real life data science problems.
Top Skills You Will Learn as Data Scientist
Job Opportunities in Data Science & Analytics
Who Is This Program For?
Minimum Eligibility for Data Science with Python
- Lectures 47
- Quizzes 0
- Duration 150 hours
- Skill level All levels
- Language English
- Students 368
- Assessments Yes
- Installation & Environment Setup
- Interactive Shell & User interface or IDE
- Variables & Data Types
- Operators in Python
- Conditional Statements
- Loops & User Input
- File Handling
- Exception Handling
- Advance Oops Concepts & Application
- Database Handling in Python
- Multi threading
Data Science with Python Basics
Algorithms & Analysis
- Data Visualization
- Exploratory data analysis – Seaborn, Univariate, Bi Variate & Multivariate Analysis.
- Linear Regression
- Optimization Algorithm
- Logistic Regression
- Performance Measurement
- Naive Bayes Algorithm
- Decision Tree Classifier
- Random Forest Classifier
- Support Vector Machines
- Time Series Analysis
- Problem Statement and Analysis
- Principal Component Analysis