Data science is a multidisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It combines expertise from areas such as statistics, mathematics, computer science, domain knowledge, and data visualization to analyze and interpret complex data.
In this course we will give you a strong understanding of how to start with data with course revolving around working on various algorithms and libraries. At the end of this course you will be able to make multiple projects of Data Science.
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Career Growth Prospects
The career growth prospects in data science are highly promising due to the increasing demand for data-driven decision-making across industries. Here's an overview of the opportunities and growth trajectory:
Strong Job Demand
The demand for data science professionals is consistently growing across sectors such as healthcare, finance, technology, retail, and more.
Diverse Career Pathways
Data science offers a wide range of roles based on expertise and interests, such as: Data Analyst, Data Scientist, Data Engineer, Machine Learning Engineer, Business Intelligence Analyst, AI Specialist.
Competitive Salaries
Data science professionals are among the highest-paid in the tech industry. Entry-level positions often start with lucrative packages, and salaries increase significantly with experience.
Opportunities for Specialization
Professionals can specialize in areas like: Natural Language Processing (NLP), Computer Vision, Big Data Engineering, Deep Learning.
Key Takeaways
Industry recognized Certificate
Industry led Content
Lifetime access to LMS
Notes, Codes & Lectures
Value addition in Resume
Learn from Industry Leaders
Doubt Clearing Support
Course Curriculum
Introduction
Overview of Datascience,
Starting with Data Science,
Overview of this training,
Overview of Data Science,
Terminologies in Data Science
Machine Learning
Introduction to Machine Learning,
What is Machine Learning?,
Types of Machine Learning,
How Machine Learning works?
Usage of Machine Learning.
Revisiting Python
Introduction to Python Programming,
Introduction to Python and Anaconda,
Working on Jupyter IDE,
Conditional statements in Python,
Different types of data,
List,Tuple Dictionary,
Loops
Function & Packages
Function,
Packages,
Installing different packages of Python
Python Libraries
Working on Various Python Libraries,
NumPy,
Pandas,
Matplotlib,
Scikit-learn
NumPy
Working on NumPy,
Introduction to Numpy Array,
Creating arrays of different Dimensions,
Indexing,
Data processing using Array
Pandas
Data Analysis using Pandas,
Introduction to Pandas,
Data Type of Pandas,
Creating Dataframe using Pandas,
Importing Dataset using Pandas,
Various operations on data using Pandas tools
Matplotlib
Data visualization using Matplotlib,
Plotting data using Matplotlib library,
Using various tools of Matplotlib,
Types of Graph,
Implementation of different types of Graphs
Scikit-Learn
Applying Algorithms on Dataset using Scikit-learn,
Data splitting,
Using different algorithms for dataset,
Prediction,
Score check
Machine Learning Algorithms
Regression analysis
Simple linear regression
Multi linear regression
Classification
Binary class classification
Multi class classification
Support Vector Machine
KNN algorithm
Strong Job Demand
Diverse Career Pathways
Competitive Salaries
Opportunities for Specialization
Courses