Osmania Technology Business Incubator

Learning Path

Non-Linear: Random Order

About the Course

Course Description
Data Science is the study of the generalizable extraction of knowledge from data. This course serves as an introduction to the data science principles required to tackle data-rich problems in business and academia, including: Statistical Interference, Machine Learning, Machine Learning algorithms, Classification techniques, Decision Tree, Clustering, Recommender Engines, Text Mining & Time series.

Course Objective
The Data Science course enables you to gain knowledge of the entire life cycle of Data Science, analyze and visualize different data sets, different Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, and Naive Bayes.

Course Study Materials

Pre-Learning Assessment for Adaptive Learning

Pre-Learning Assessment for Adaptive Learning 15 Questions

Module 1 : Introduction

1.1 Introduction to Data Science -Evolution of Data Science
1.2 Business Intelligence vs Data Science - Life cycle of Data Science
1.3 Tools of Data Science
1.4 Introduction to Big Data and Hadoop
1.5 Introduction to R
1.6 Introduction to Machine Learning
Introduction 10 Questions

Module 2 : Statistical Interference

2.1 Statistical Inference - Terminologies of Statistics
2.2 Measures of Centers - Measures of Spread
2.3 Probability -Normal Distribution
2.4 Data Analysis Pipeline
2.5 Data Extraction - Introduction -Types of Data Raw and Processed Data
2.6 Data Wrangling
2.7 Exploratory Data Analysis - Visualization of Data
Statistical Interference 10 Questions

Module 3 : Machine Learning

3.1 Introduction to Machine Learning
3.2 Machine Learning Use-Cases -Machine Learning Process Flow
3.3 Machine Learning Categories
Machine Learning 10 Questions

Module 4 : Machine Learning Algorithms

4.1 Three Basic Machine Learning Algorithms - Linear Regression
4.2 k-Nearest Neighbors (k-NN)
4.3 k-means
4.4 Supervised Learning algorithm Logistic Regression
Machine Learning Algorithms 10 Questions

Module 5 : Classification techniques

5.1 Classification Techniques -Decision Tree - Introduction
5.2 Algorithm for Decision Tree Induction
5.3 Creating a Perfect Decision Tree
5.4 Confusion Matrix
5.5 Random Forest - Introduction
5.6 Navies Bayes
5.7 Support Vector Machine Classification
Classification techniques 10 Questions

Module 6 : Clustering

6.1 Unsupervised Learning - Clustering & its use cases
6.2 K-means Clustering
6.3 C-means Clustering
6.4 Canopy Clustering
6.5 Hierarchical Clustering
Clustering 10 Questions

Module 7 : Recommender Engines

7.1 Recommender Engines
7.2 Types of Recommendations
7.3 User-Based Recommendation
7.4 Item-Based Recommendation
7.5 Difference User-Based and Item-Based Recommendation -Recommendation use cases
Recommender Engines 10 Questions

Module 8 : Text Mining

8.1 Text Mining - Concepts of text-mining - Use cases
8.2 Text Mining Algorithms -Quantifying text - TF-IDF- Beyond TF-IDF
Text Mining 10 Questions

Module 9 : Time Series

9.1 Time Series - Time Series data
9.2 Different components of Time Series data
9.3 Visualize the data to identify Time Series Components
9.4 Implement ARIMA model for forecasting
9.5 Exponential smoothing models
Time Series 10 Questions

Final Assessment

Final Assessment 20 Questions

The certificate issued for the Course will have the student's Name, Photograph, Course Title, Certificate number, Date of course completion and the name(s) and logo(s) of the Certifying Bodies. Only the e-certificate will be made available. No Hard copies. The certificates issued by uLektz Learning Solutions Pvt. Ltd. can be e-verifiable at www.ulektzskills.com/verify.

Students are required to take online assessments with eProctoring.
Students will be assessed both at the end of each module and at the end of the Course.
Students scoring a minimum of 50% in the assessments are considered for Certifications