uLektz Skills

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
1.7 Applications of Data Science in Various Fields
1.8 Data Security Issues
INTRODUCTION 10 Questions

Module 2 : DATA COLLECTION AND DATA PRE-PROCESSING

2.1 Data Collection Strategies
2.2 Data Pre-Processing Overview
2.3 Data Cleaning
2.4 Data Integration and Transformation
2.5 Data Reduction
2.6 Data Discretization
2.7 Model Building
2.8 Deployment
2.9 Ethics of Data Science
DATA COLLECTION AND DATA PRE-PROCESSING - Assessment 10 Questions

Module 3 : STATISTICAL INFERENCE

3.1 Statistical Inference - Terminologies of Statistics
3.2 Measures of Centers - Measures of Spread
3.3 Probability - Normal Distribution
3.4 Point Estimation
3.5 Confidence Intervals
3.6 Data Analysis Pipeline
3.7 Hypothesis Testing - Type I and Type II Errors
3.8 Data Extraction - Introduction - Types of Data Raw and Processed Data
3.8 Data Extraction - Introduction - Types of Data Raw and Processed Data
3.9 Data Wrangling
3.10 Exploratory Data Analysis - Visualization of Data
STATISTICAL INFERENCE - Assessment 10 Questions

Module 4 : MACHINE LEARNING

4.1 Introduction to Machine Learning
4.2 Machine Learning Use-Cases -Machine Learning Process Flow
4.3 Model Evaluation and Selection
4.4 Neural Networks and Deep Learning
4.5 Natural Language Processing (NLP)
MACHINE LEARNING - Assessment 10 Questions

Module 5 : THREE BASIC MACHINE LEARNING ALGORITHMS - LINEAR REGRESSION

5.1 Three Basic Machine Learning Algorithms - Linear Regression
5.2 k-Nearest Neighbors (k-NN)
5.3 k-means
5.4 Supervised Learning algorithm Logistic Regression
THREE BASIC MACHINE LEARNING ALGORITHMS - LINEAR REGRESSION - Assessment 10 Questions

Module 6 : CLASSIFICATION TECHNIQUES -DECISION TREE

6.1 Classification Techniques - Decision Tree - Introduction
6.2 Algorithm for Decision Tree Induction
6.3 Creating a Perfect Decision Tree
6.4 Confusion Matrix
6.5 Random Forest - Introduction
6.6 Navies Bayes
6.7 Support Vector Machine Classification
CLASSIFICATION TECHNIQUES -DECISION TREE - Assessment 10 Questions

Module 7 : UNSUPERVISED LEARNING - CLUSTERING & ITS USE CASES

7.1 Unsupervised Learning - Clustering & its use cases
7.2 K-means Clustering
7.3 C-means Clustering
7.4 Canopy Clustering
7.5 Hierarchical Clustering
7.6 Density-Based Clustering: DBSCAN
7.7 Model-Based Clustering: Gaussian Mixture Models (GMM)
7.8 Cluster Evaluation and Validation
7.9 Use Cases of Clustering
7.10 Limitations and Challenges of Clustering
UNSUPERVISED LEARNING - CLUSTERING & ITS USE CASES - Assessment 10 Questions

Module 8 : RECOMMENDER ENGINES

8.1 Recommender Engines
8.2 Types of Recommendations
8.3 User-Based Recommendation
8.4 Item-Based Recommendation
8.5 Difference User-Based and Item-Based Recommendation -Recommendation use cases
8.6 Recommender Systems in Practice
8.7 Collaborative Filtering
8.8 Content-Based Filtering
8.9 Evaluation Metrics for Recommender Systems
8.10 Recommender Systems in Practice
RECOMMENDER ENGINES - Assessment 10 Questions

Module 9 : TEXT MINING - CONCEPTS OF TEXT-MINING - USE CASES

9.1Text Mining - Concepts of Text-mining - Use cases
9.2 Text Mining Algorithms -Quantifying text - TF-IDF- Beyond TF-IDF
9.3 Named Entity Recognition (NER)
9.4 Text Classification and Text Clustering
9.5 Ethical Considerations in Text Mining
TEXT MINING - CONCEPTS OF TEXT-MINING - USE CASES - Assessment 10 Questions

Module 10: TIME SERIES - TIME SERIES DATA

10.1 Time Series - Time Series data
10.2 Different components of Time Series data
10.3 Visualize the data to identify Time Series Components
10.4 Implement ARIMA model for forecasting
10.5 Exponential Smoothing Models
10.6 Seasonal and Time Series Decomposition
10.7 Advanced Time Series Techniques
10.8 Long Short-Term Memory (LSTM) Networks for Time Series
10.9 Time Series Data for Machine Learning
10.10 Case Study: Forecasting Time Series with ARIMA
TIME SERIES - TIME SERIES DATA - Assessment 10 Questions

Final Assessment

Final Assessment 20 Questions

The certificate issued for the Course will have

Student's Name
Photograph
Course Title
Certificate Number
Date of Course Completion
Name(s) and Logo(s) of the Certifying Bodies

Only the e-certificate will be made available. No Hard copies. The certificates issued by The Academic Council of uLektz. can be e-verifiable at www.ulektzskills.com/verify.

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