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Learning Path

Adaptive:Personalised Learning

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.

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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 - Assessment 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 - Assessment 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 - Assessment 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 - Assessment 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
  • Tree - Assessment 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 - 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
Final Assessment
  • Final Assessment 15 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
certificate
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₹299
Features:
  • 45 hours Learning Content
  • 100% online Courses
  • English Language
  • Certifications

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.

Course Approved

Approval Pending - In-Progress

Course access details will be shared within 24 hours.
For help contact: support@ulektz.com

Course Enrollment

Course

Course starts on 11-05-2021

Course

You have completed 6 hours of learning for 02-07-2022. You can continue learning starting 03-07-2022.

Course

This course can only be taken in sequential order.

Course

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

Course

Are you sure want to enroll this course?.

Course

Course

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Result Summary

Data Science