Data Science

Offered By: The Academic Council Of uLektz

Certified By:
The Academic Council of uLektz

45 hours of Learning Content

Maximum 1 months.

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. 

 

 

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

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 - Assessment

    5 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 - Assessment

    5 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 - Assessment

    8 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 - Assessment

    8 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

  • Tree - Assessment

    5 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 - Assessment

    9 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 Engine - Assessment

    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 - Assessment

    9 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 - Assessment

    10 Questions

Certificate

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 can be e-verifiable at www.ulektz.com/skills.

  • 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
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Course

Registration opens on 04-02-2019

Course

Your registration details are under review. It should take about 1 to 2 working days. Once approved you will be notified by email and then you should be able to access the course.

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For help contact: support@ulektz.com

Course

Course starts on 10-07-2019

Course

You have completed 6 hours of learning for 15-11-2019. You can continue learning starting 16-11-2019.

Course

This course can only be taken in sequential order.

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You have completed the course. You will be notified by email once the certificate is generated.

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