APPLIED DATA SCIENCE WITH MACHINE LEARNING

1. THE FUTURE STARTS HERE Applied Data sciencewith machine learning Supported by Rethinking business, technology and data.

2. 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?

3. Retail Health care Analyze consumer behavior - Predict product pricing - - Detect causes of disease - Risk prediction Medical Enterprise Disease prediction - Analyze patient’s recovery growth - - Capturing data volume, variety and value DATA SCIENCEin various industries Insurance Machine learning Fraud & Risk detection - Insurance claim prediction - - Data science is leveraged to disrupt ML based application Funding Digital marketing - Detect targeted audience - Demographic behavior analysis Detect possible fundraisers -

4. Data Science is aboutTelling a story

5. Frame the problem Explore the data • Ask a lot of questions • Identify business priorities • Split, Segment & Plot the data • Identify Patterns & extract features Collect Raw Data Perform in-Depth Analysis • Identify available datasets • Extract data into usable format • Create a predictive model • Evaluate & Refine model The process Process the Data Communicate Results • Examine Data at a high level • Clean the data • Identify Business insights • Visualize your findings • Tell a clear & actionable story

6. 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 $5M+ More than 20 % 40 % + +10 % to +20 % +30 % $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 % +20 % < $500K +10 % 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 dollars Source - Deloitte

7. 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 data..  Besides the rise of data online, the demand for data scientists is also linked to new technologies, including Artificial Intelligence. #2 #1 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.

8. 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

9. 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 problems.. Program overview Full Time – 14 weeks Part Time – 24 weeks Duration

10. 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 insights 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 drivers

11. Core Concepts of Machine Learning Applied Data Science with Machine Learning Why you should choose our course? Strengthening the Basics Python for Data Science 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. You’ll  also leave with an understanding of how to integrate Devops Methodology with Data Science Interactive Sessions With real Data scientists Practical Sessions Deep Learning Focus

12. Applied data science with machine learning What you’ll learn Data science Fundamentals Data Analytics Data Engineering Machine Learning for Data Science Data Visualization Capstone Project

13. 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

14. Applied data science with machine learning Data wrangling and exploratory Data Analysis 1 3 Learn important Python based packages which can help us to perform Data Analytics and Wrangling. Transform and slice the Data frames 4 Use visualization tools to perform Exploratory Data analysis to be presented to the stakeholders 2 Use Python Visualization Packages to Perform Exploratory Data Analysis which is an Important step in analysing the Data in Data Science 5 Export the visualization in required formats

15. 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

16. 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 Feature Engineering Model Building Model in Production Data Scaling Feature Selection Model Validation Data Pre-processing Feature Elimination 1 Learn Supervised and Unsupervised Machine learning Algorithms How to perform Dimensionality reduction using PCA 2 Perform Feature Selection to get better model output 3 Evaluate the Machine Learning model you have built 4 5 Learn how to Interpret the model

17. 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 Applications. 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.

18. 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 inference. 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

19. 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 incorporated. Data science fundamentals Data science fundamentals Data science fundamentals Data science fundamentals Data Extraction 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 others. Successful completion of the project will enable you to receive recognition from the institute and pave the way to the Data Science Job market

20. Applied data science with machine learning Part time – weekly breakdown WEEK 20-24 Capstone/internship 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

21. THE FUTURE STARTS HERE Applied Data Science with Machine Learning Supported by Bridging dimensions Thank You Code, Craft & Create