# Master the Fundamentals of Data Science - Advanced

## Certified By: NITTTR Chandigarh, Ministry of Human Resources Development

45 hours of Learning Content

Maximum 1 months.

The objective of this course is to impart necessary knowledge of the mathematical foundations needed for data science and develop programming skills required to build data science applications. At end of this course, the students will be able to demonstrate understanding of the mathematical foundations needed for data science ,collect, explore, clean, munge and manipulate data. Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks and clustering , build data science applications using Python based toolkits.

Skills Required:

Though, there is no particular prerequisite to learn Cyber Security, you will be able to do very well, if you have these basic skills:

Basic Programming Knowledge
Good command in Maths and Statistics
Data Visualisation skills
Good communication skills.

### Course Study Materials

#### Module 1 : Introduction to Data Science

• 1.1 Introduction to Data Science :Concept of data science

Duration:
• 1.2 Traits-of-big-data

• 1.3 Web Scraping- Analysis-vs-Reporting

• Introduction to Data Science - Assessment

5 Questions

#### Module 2 : Introduction to Programming Tools for Data Science

• 2.1 Introduction to Programming Tools for Data Science Toolkits using Python Matplotlib, NumPy, Scikit-learn, NLTK

• 2.2 Visualization of Data Bar Charts, Line Charts, Scatterplots

• 2.3 Working with data Reading Files, Scraping the Web, Using APIs (Example Using the Twitter APIs), Cleaning and Munging

• 2.4 Manipulating Data, Rescaling, Dimensionality Reduction

• Introduction to Programming Tools for Data Science - Assessment

5 Questions

#### Module 3 : Mathematical Foundations

• 3.1 Mathematical Foundations Linear Algebra Vectors, Matrices

• 3.2 Statistics Describing a Single Set of Data

• 3.3 Probability - Dependence and Independence

• 3.4 Hypothesis and Inference Statistical Hypothesis Testing

• Mathematical Foundations - Assessment

8 Questions

#### Module 4 : Machine Learning Concepts

• 4.1 Overview of Machine learning concepts - Over fitting and train test splits

• 4.2 Types of Machine learning - Supervised Learning, Unsupervised Learning

• 4.3 Introduction to Bayes’s Theorem, Linear Regression, model assumption, Regularization (lasso, ridge, elastic net), Classification and Regression algorithms

• 4.4 Navie Bayes, k-Nearest Neighbors (k-NN), Logistic regression, Support Vector Machine(SVM)

• 4.5 Decision Tree and Random Forest, Classification Errors

• 4.6 Analysis of Time Series- Linear Systems Analysis, Nonlinear Dynamics, Rule Induction

• Machine Learning Concepts - Assessment

8 Questions

#### Module 5 : Case Studies of Data Science Application

• 5.1 Case Studies of Data Science Application -Weather forecasting, Object recognition, Real Time Sentiment Analysis

• 5.1 Case Studies of Data Science Application -Weather forecasting, Object recognition, Real Time Sentiment Analysis - Assessment

4 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
##### 100% online Course

Access from anywhere, anytime

##### English Language

Easy to understand simplified text & video content

##### Certifications

Official recognition for better job placements and careers

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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.

## Course

Course starts on 03-08-2019

## Course

You have completed 6 hours of learning for 15-09-2019. You can continue learning starting 16-09-2019.

## 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.

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