Data Science Analyst

What is Data Science Analyst?

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

Introduction

You will learn:

• The basics of Python programming, one of the most widely used programming languages for data science and machine learning.
• How to apply the knowledge and use Python to analyze 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 exciting field. However, we recommend candidates 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 skills.

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-hour course with 9 modules, including hands-on experience with real-life projects like movie recommendations, house price predictions, 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 through everything 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

This module introduces data analysis using Python. Students will learn to deal with missing data, format messy data correctly for analysis, and answer questions based on analysis results.

• 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

This module teaches students how to visualize data using different kinds of plots, which is crucial for communicating analysis results.

• 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 students to battle-tested machine learning algorithms used daily by businesses and organizations 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 buzzword in the tech industry today. This module introduces students to AI and its application to real-world problems such as 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. This module covers the Apache Spark framework and its use 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 guides students in 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 Authorization
• Exercises

Module 8: Final Capstone Project

The capstone project allows students to demonstrate the knowledge acquired in a real-world scenario. Students will work towards a project objective and deadline. Grading will be done, and certificates awarded.

• Project Details
• Submission
• Grading
• Certificate

Why choose Tip Technology?

• Tip Technologies provides courseware with presentation slides, practice questions, and hands-on labs.
• We offer free coaching sessions to help you focus and excel in the direction of your strengths.
• We provide lifetime 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 organizations around the world.

Benefit for Employees:

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

Need Help?

For inquiries, contact admin@grantedcs.com. Our expert advisors will be in touch.