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Applied Data Science and Machine Learning


With outstanding and renowned faculty, MAGES Institute brings you a highly competitive Applied Data Science and Machine Learning Course in which in the first 1-3 weeks you'll learn about data science fundamentals, then in the next 4-9 weeks you'll master data analytics and data engineering. From week 10-12 you'll learn data visualization which will be followed by machine learning in week 13-19. It will end with a capstone/internship in weeks 20-24. – PowerPoint PPT presentation

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Title: Applied Data Science and Machine Learning

Applied Data science with machine learning
Supported by
Rethinking business, technology and data.
Need for Data Science
Better Decision Making
Data Science is Mainly Needed For
Predictive Analysis What Happens Next ?
Pattern Discovery ? Is there any hidden
Information in the Data?
Health care
Analyze consumer behavior - Predict product
pricing -
- Detect causes of disease - Risk prediction
Disease prediction - Analyze patients recovery
growth -
- Capturing data volume, variety and value
DATA SCIENCEin various industries
Machine learning
Fraud Risk detection - Insurance claim
prediction -
- Data science is leveraged to disrupt ML based
Digital marketing
- Detect targeted audience - Demographic
behavior analysis
Detect possible fundraisers -
Data Science is aboutTelling a story
  • Split, Segment Plot the data
  • Identify Patterns extract features
  • Ask a lot of questions
  • Identify business priorities
  • Create a predictive model
  • Evaluate Refine model
  • Identify available datasets
  • Extract data into usable format

The process
  • Identify Business insights
  • Visualize your findings
  • Tell a clear actionable story
  • Examine Data at a high level
  • Clean the data

Applied data science with machine learning
Organization are spending on AI technologies and
seeing a return on their investment
Why the industries need Data scientist?
Investment in current fiscal year
Investment change in next fiscal year
Return from AI investment to date
More than 20
10 to 20
500K - 5M
  • Rapid market growth is evident, and Organizations
    indicate they are increasingly spending on AI
    technologies and getting positive returns
  • This will lead to higher demand for Data
    Scientist across the Industries

1 to 9
lt 500K
Stay the same
Notes Percentages may not total 100 percent due
to not including all answer choices fromall
questions all monetary amounts are given in US
Source - Deloitte
Applied data science with machine learning
Why the demand for data scientists is high
Ever since the internet has taken the spotlight,
data has become key for companies across
industries. Every action that a user takes online
can now be tracked, leading to huge amounts of
Besides the rise of data online, the demand for
data scientists is also linked to new
technologies, including Artificial Intelligence.
Artificial Intelligence
Big Data
Big data and AI are the major factors in the
growing demand for data scientists, but other
related tech trends are also driving the need.
For example, the internet of things and deep
learning are just two trends that are very
realistic for 2020.
Applied data science with machine learning
2,7 MillionCareer opportunities Estimated for
Data Science and analytical roles in 2020
Salary Trend
Top industries
Jobs requiring Data Science skills are paying
an average of 114,000. Advertised data scientist
and data engineering jobs pay an average of
105,000 and 117,000 respectively
  • Prominent economic sectors where data analytics
    is marking its presence include
  • Energy
  • Insurance
  • Healthcare
  • Retail Banking

28 Annual Growth In job opportunities for data
scientist, data developers and data engineers
across the globe
Job titles include
  • Data Scientist
  • Data Analytics Manager
  • Data Architect
  • Principal Scientist
  • Data Engineer

Applied data science with machine learning
Data science immersive course offers students the
opportunity to advance their careers and gain
skills for the new digital economy. Students
will learn how to use the right software and
techniques to read visual and statistical data
and present it in a way that solves real world
Program overview
Full Time 14 weeks Part Time 24 weeks
Applied Data Science with Machine Learning
Course Overview
The course is designed to train the data science
aspirants on the core skills sets that is
required to
Learn the Technical Stack
Understand the Concepts
Implement the Learned Concepts
Able to extract, transform and load data and use
visualization techniques to derive actionable
Able to utilize statistical methods in the data
driven decision-making process
Able to leverage tools to develop business data
processing and visualization pipelines
Able to create predictive models using AI and
Machine Learning techniques
Combined with Industry based use cases and
examples , this course will enable you as Data
Science professional to work in Companies where
Analytics and Data science forms the core growth
Applied Data Science with Machine Learning
Why you should choose our course?
From Python to Machine Learning, our 24-week data
science training program gives you the breadth
and depth needed to become a well-rounded data
scientist. Youll  also leave with an
understanding of how to integrate Devops
Methodology with Data Science
Applied data science with machine learning
What youll learn
Data science Fundamentals
Data Analytics
Data Engineering
Machine Learning for Data Science
Data Visualization
Capstone Project
Applied data science with machine learning
Data Science Fundamentals
Built on the Top of the Basic Python Knowledge.
In this module focus will be train the aspirant
on various concepts of python that are required
for Data science and Machine Learning
  • Parallel Processing
  • Advanced packages
  • Advanced Data types
  • Algorithms
  • Learn to use the Python components Efficiently
  • Best Practise in Coding
  • Understand the Usage
  • Use Python to Extract Data
  • DB Connect
  • Web Scrapping
  • Objects and Classes
  • Instances
  • Methods
  • Inheritance
  • MRO Framework
  • Learn how to use Functions
  • Generators
  • Decorators
  • Recursive Functions

Applied data science with machine learning
Data wrangling and exploratory Data Analysis
Learn important Python based packages which can
help us to perform Data Analytics and Wrangling.
Transform and slice the Data frames
Use visualization tools to perform Exploratory
Data analysis to be presented to the stakeholders
Use Python Visualization Packages to Perform
Exploratory Data Analysis which is an Important
step in analysing the Data in Data Science
Export the visualization in required formats
Applied data science with machine learning
Machine learning what is it?
Machine learning is an application of artificial
intelligence (AI) that provides systems the
ability to automatically learn and improve from
experience without being explicitly programmed
Applied data science with machine learning
Machine learning for data science - I
Learn various stages of Machine Learning model
building steps. Understand the algorithms and
implement them using Python Packages
Data Cleaning
Latent Features
Model Tuning
Data Extraction
Model in Production
Model Building
Feature Engineering
Data Scaling
Feature Selection
Model Validation
Data Pre-processing
Feature Elimination
Learn Supervised and Unsupervised Machine
learning Algorithms
How to perform Dimensionality reduction using PCA
Perform Feature Selection to get better model
Evaluate the Machine Learning model you have built
Learn how to Interpret the model
Applied data science with machine learning
Part Time Weekly Breakdown
WEEK 1-3
WEEK 4-9
Data science Fundamentals
Data analytics Data engineering
Learn the Programming fundamentals and nuances
of Python Language. Understand how to use the
power of Python to analyze data and create useful
Learn how to ingest data from various sources
and will learn to work with modules such as
Pandas module on performing data
wrangling. Learn to Extract the data from
Database and ingest it into python as Dataframe
and perform analysis.
Applied data science with machine learning
Part time weekly breakdown
WEEK 10-12
WEEK 13-19
Data visualization
Machine learning
Learn the concept of Exploratory Data analysis
(EDA) and how to use visualization techniques.
Students will learn how to plot data using
various visualization techniques. Learn how to
provide analytical inference to the EDA and
visualizations. Learn how to perform
statistical test on the dataset and provide
Learn the feature engineering techniques and how
it impacts the process in Machine learning model
building. Learn the concepts of supervised and
unsupervised learning and types of algorithms
used for Different Scenarios Students will learn
the concepts of NLP Students will learn the
concepts of DevOps and how to use it to
productize a Predictive Model
Applied data science with machine learning
Capstone Project
The Capstone Project is designed to test the
learnings on various steps involved in building a
Machine learning model. The Project problem
statement is based on real world scenario where
the challenges and complexities will be
Data science fundamentals
Data science fundamentals
Data science fundamentals
Data science fundamentals
Data Extraction
Successful completion of the project will enable
you to receive recognition from the institute and
pave the way to the Data Science Job market
The learners have to complete objectives in each
of these steps in the ML process to test their
understanding the concepts and their skill sets
in implementing them using correct algorithms
Real Work Scenarios will be based on Industrial
use cases such as Churn Prediction , Fraudulent
Transaction Prediction , Segmentations and
Applied data science with machine learning
Part time weekly breakdown
WEEK 20-24
Capstone Studio Practice is a research-based
module that integrates concepts and work from
throughout the program
Students will perform Extensive EDA on the data
and provide inference
  • Software/Tech Used
  • Python 3.x and Above - Anaconda Version Numpy
  • Pandas
  • Matplotlib
  • Scikit Learn
  • Keras
  • TensorFlow 2.0 and dependencies Jupyter or
    Spyder (Part of Python Anaconda Release) Git
  • Jenkins
  • Docker

Students will Analyze the data and construct a
machine learning model to predict based on the
business case provided in the project
Capstone Project focus on training the students
to have an end to end knowledge on Data Analysis
and Data science
Students will extract data from Data source and
ingest into the required format
Students will fine tune the model and provide
final predictions
Applied Data Science with Machine Learning
Supported by
Bridging dimensions
Thank You
Code, Craft Create
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