Includes
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Duration:
9 months 25 days
(Avg)
Features
Full lifetime access
Access on mobile
Assignments
Certificate of Completion
Overview
Course Description
Program Curriculum :
Preparatory Course:
EXCEL FOR DATA ANALYTICS : Statistical Functions, Logical Functions, Mathematical Functions, Lookup Functions, Charts, Dashboards, What-if Analysis, Forecasting, Linear Regression Analysis etc
TABLEAU AND POWER BI : Charts, Maps, Level of Details Expressions, Table Calculations, Aggregations, Granularity, Data Extracts, Filters, Dashboards and Storyboards in Tableau & Power BI.
PYTHON AND R PROGRAMMING : Variables, Operators, Strings, Data Structures, Loops, Conditionals, Functions, Regular Expressions, OOPs, Space & Time Complexity in Python, and Intro to R Programming Preparatory Course
Data Analysis Course:
STATISTICS & HYPOTHESIS TESTING : Descriptive Statistics, Basic and Conditional Probability, Inferential statistics, Normal distribution, Hypothesis testing, t test, z test, ANOVA, chi-squared
DATA ANALYSIS AND VISUALIZATION : Introduction to Numpy, Pandas, Matplotlib, Seaborn, Basic Charts for Advanced Data Visualization, Interactive Visualization, 3D Charts and Visualization.
DATA CLEANING, PREPARATION, & PROCESSING : Missing Values treatment, Outliers treatment, Categorical encoding, Data Manipulation functions, Data transformations, feature engineering, and feature scaling. Data Analysis Course
Machine Learning Course:
SUPERVISED LEARNING : Linear Regression, Logistic Regression, KNN, SVM, Decision Trees, Random Forests, Boosting Algorithms, Imbalanced ML, and Advanced ML Modelling Techniques.
UNSUPERVISED LEARNING : K Means Clustering, Hierarchical Clustering, Dimensionality Reduction, Principal Component Analysis, Linear Discriminant Analysis, Clustering metrics etc.
TIME SERIES & RECOMMENDER SYSTEMS : Time series fundamentals, Introduction to AR, MA, ARMA, ARIMA, SARIMA, ARIMAX, SARIMAX Models, Introduction to Recommender systems using Surprise Package.
Big Data and SQL Course:
INTRO TO MY SQL AND DATABASES : Database fundamentals, DDL, DML, DQL Queries, SQL Joins, Sub queries, Set operations, Accessing databases, Loading Tables, Intro to Spark for Big Data.
QUERY ANALYSIS IN PYTHON : Introduction to Query analysis in Python using Pandas, and Pyspark library. Exploring the pyspark library to execute any kind of query in Python Programming.
INTRO TO SPARK FOR BIG DATA : Introduction to Big Data, and Spark Architecture system, Installation and setup of Spark. Big Data Analysis, and Execution of Machine Learning Algorithms.
Positions offered after pursuing the course :
You can bceome a
- Data Scientist
- Data Analyst
- Data Engineer
- ML Engineer
- NLP Engineer
- Business Analyst
- Data Architect
- Database Administrator
- Statistician
What you'll learn
- In-depth knowledge of Data Science Concepts, Data Cleaning, Processing, ML, DL, and NLP algorithms.
- Hands-on Experience with advanced Data Science and Business Intelligence Tools and Techniques.
- Ability to solve Real-world Data Science problems and making data-driven decision making for optimized results.
- Networking Opportunities with fellow students, faculties, Domain Experts, Seasoned data Science Professionals.
- Enhanced Career Opportunities, as demand for Data Science professionals is very high across all the Industries.
Requirements
- Graduation required
