DATA SCIENCE

What is DATA SCIENCE?

Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. Data Science skill, is in high demand by businesses and organisations around the world.

Introduction

You will learn:

The basics of Python programming which is the widely used programming languages for data science and machine learning.

How to apply the knowledge and use Python to analyse data and visualize data.

Battle-tested machine learning algorithms such as random forest and support vector machine.

Artificial intelligence and deep learning algorithms used for image recognition.

Prerequisite

This course is designed for people with no prior experience who want to get started and get hired in this interesting field, But we recommend candidates should have the following:

Basic PC operating system navigation skills
Basic Internet usage skills

Who Should Attend?

We recommend this course for anybody looking for a career in IT or looking to acquire Data Analysis skill.

This course was specially curated by experts in the field with over 10+ years’ experience. You will be guided by our mentor with hands-on experience until you get that data science job! So what are you waiting for?

Course Structure

This is a 30 hrs course with 9 module, it would include hand-on experience with real life projects like movie recommendations, predictions house prices, fraud detection, customer churn detection and many more

Course Content

Module 1: Understanding Python Programming

This module introduces the student to the world of Python. We will guide you along in the things you need to know to get started from setting up your environment to basic topics needed for data science and AI.

Python Environment setup
Jupyter Notebooks
Data Types
Comparison Operators
For Loops
While Loops
List Comprehension
range()
Functions
Lambda expressions
Map and filter
Exercises

Module 2: Data Analysis with Python

The student is introduced to data analysis using Python. The student will learn deal with missing data, put messy data in right formats for analysis and answer questions from result of analyses.

Numpy Arrays
Numpy Operations
Indexing Arrays
Introduction to Pandas
Pandas Series
Pandas DataFrames
Dealing with Missing Data
Grouping data
Merging and Concatenating Data
Exercises

Module 3: Data Visualization

In this module, the student will learn how to visualize data using different kinds of plots which is important in communicating the results of analyses.

Introduction to Matplotlib
Introduction to Seaborn
Scatter Plots
Matrix Plots
Categorical Plots
Distribution Plots
Interactive plots with Plotly
Exercises

Module 4: Machine Learning

This module introduces the student to battle-tested machine learning algorithms used daily by businesses and organisations for solving real-world problems.

Simple Linear Regression
Multiple Linear Regression
Logistic Regression
Support Vector Machine
Random Forest
K Means Clustering
Recommender Systems
Natural Language Processing
Exercises

Module 5: Deep Learning and Artificial Intelligence

Artificial Intelligence has become a buzz word in the tech industry today but what exactly is it? This module introduces the student to AI and how to use for real world problems such image recognition.

What is Deep Learning?
Installing Tensorflow
The Backpropagation Algorithm
What are Convolutional Neural Networks?
ReLu and Pooling
Flattening
Cross Entropy
Practical Image Recognition
Exercises

Module 6: Big Data with Python

Dealing with very big data in terabytes and petabytes is a regular task in the industry and an important skill for the data scientist. In this module, the student will learn about the Apache Spark framework and how to use it for large data processing and machine learning.

Local and Cloud Environment Setup
Introduction to Spark and PySpark
Spark DataFrames
Machine Learning with Spark and MLib
Linear Regression with PySpark
Random Forest with PySpark
K Means Clustering
Recommender Systems with PySpark
Exercises

Module 7: Interactive Dashboards with Dash and Plotly

Showing the results of your data science work using real-time interactive dashboards is an often overlooked but very important part of data science and AI. This module shows guides the students in the process of developing an interactive web application for a data science project useful for live client demonstrations.

Plotly Basics
Introduction to Dash
Dash Layouts
Dash Components
Interacting with your App
App Authorisation
Exercises

Module 8: Final Capstone Project

The capstone project is an opportunity for the student to demonstrate the knowledge acquired in a real world scenario. Student will work towards project objective and deadline. Grading will be done and certificates awarded.

Project Details
Submission
Grading
Certificate

Why choose Tip Technology?

Tip Technologies provide you course-ware with presentation slides, practice questions, and hands-on-labs
We offer free coaching session to help you focus and excel in the direction of your strength
We provide life time support so you never feel alone
Get practical experience with real-world examples

Advantages of DATA SCIENCE

Benefit for Employers:

Ensures your employees have the Data Analysis knowledge they need in today’s Data rich environment
Shows your business is committed to having capable IT support personnel
Data science is one of the most in demand skills widely needed by businesses and organisations around the world

Benefit for Employers:

It offers great salaries
practitioners get to solve interesting real-world problems such as movie recommendations, predictions house prices, fraud detection, customer churn detection and many more

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