Data Science Syllabus
Data Science Fundamental Course
Welcome to “Practical Data Science” course!
This course is designed to equip you with the necessary skills to become a data scientist , one of the most in-demand roles in the tech industry.Our comprehensive curriculum covers the entire data science process,from data collection to data analysis to machine learning.
Course Overview:
Our comprehensive course covers 10 modules,providing you with a strong foundation in Data Science tools and techniques.
Module1: Introduction to Data Science and its importance
In this module,you’ll learn why data science is important and how it has become a driving force in the tech industry.You’ll also understand the data science process,the different roles in the market,and the tools and technologies used in the industry.
Module2 : Basics of Python Programming (with exercises and examples)
In this module,you’ll learn the basics of Python programming,one of the most popular languages for data science.You’ll cover datatypes,operators,control statements,loops,functions,and more.You’ll also learn how to use various modules in Python,such as os,regular expressions,and web scraping.
Module3 : Introduction to Git
In this module,you’ll learn about version control with Git and GitHub.You’ll understand the importance of version control and learn basic Git commands for version control.
Module 4 : Introduction to SQL
In this module, you’ll learn how to use SQL to query and manipulate data.You’ll cover basic SQL commands for data manipulation,as well as connecting to databases using Python.
Module 5 : Introduction to Data Analysis with Python
In this module,you’ll learn how to use Pandas and Numpy for data analysis.You’ll learn how to manipulate and analyze data,as well as basic statistical analysis with Pandas.
Module 6 : Introduction to Data Visualization with Python
In this module,you’ll learn the basics of data visualization using Matplotlib and Seaborn.You’ll learn how to create basic visualizations and understand the importance of data visualization.
Module7 : Introduction to Statistics
In this module , you’ll learn the basics of statistics , including measures of central tendency,probability distributions,hypothesis testing,and confidence intervals.
Module 8 : Introduction to Machine Learning with Python
In this module,you’ll learn about the different types of machine learning algorithms , including linear regression,logistic regression,and k-means clustering.You’ll also learn how to build and train a basic machine learning model with Scikit-Learn.
Module 9 : Introduction to Deep Learning and Natural Language Processing with Python
In this module,you’ll learn about artificial neural networks and how to build and train a basic neural network with Keras.You’ll also learn about natural language processing with an example project.
Module10 : Exploring Data Science Projects
In this module , you’ll learn about popular data science projects and applications using Kaggle and GitHub.You’ll also learn best practices for data science projects and how to package projects using cookie cutters.
This course is designed for students who are interested in a career in data science, as well as professionals who want to upgrade theirs kills.By the end of this course,you’ll have the necessary skills to build and train basic machine learning models,manipulate and analyze data,and package datascience projects.