
Applied Data Science and
Machine Learning Program
Starting soon
8-12 hrs per week
Virtual
Interested in exact details about the program?
LEAPS 10 MONTH PROGRAM
Right blend of instructor led & self paced
- The program is catered to professionals with 2-6 years of industry experience who aspire to start or grow their career in analytics & data science space
- Carefully designed and curated by industry experts and academicians, this program is application oriented and highly contextual
- The program will be delivered through LEAPS offering a highly effective blend of instructor led and self paced format
- The candidates selected for the program gets life-time FREE access to the LEAPS platform, a precious asset to have hands-on access to get skilled and be abreast with the new developments in analytics & AI
Program Outline
The program is divided into 3 learning blocks of approximately 12 to 15 weeks each, to enhance the depth and breadth of contextual learning.
Upon completion of each block, the learner would receive a completion certificate, marking a milestone of the overall progress, and followed by a live Hackathon.
Live hackathons are included in the first two learning blocks
Learning Block 1
Foundations of data science, statistics and Python programming, feature engineering and basics of supervised ML
Learning Block 2
Foundations of Machine Learning – Supervised and unsupervised ML, ensemble models & NLP
Learning Block 3
Foundations of Deep Learning and Domain Exposure (in BFS, Retail and Manufacturing), building ML based solutions
Program Overview
Program designed to enable a “Learn-by-Doing” approach to foster industry readiness
- Horizontal applied learning through data case-based curriculum; hands-on practice via both point-click and coding; aided by learning videos, reading materials, access to professors, and structured mentorship.
- Virtual coding lab to build and implement/showcase one’s ML based solutions; Practice on LEAPS’ cloud-based platform that enables faster learning, collaboration, and competitive hackathons.
- Industry vertical exposure across Banking, Retail, Manufacturing, etc.
- Industry standard virtual assessments and long-term platform access to enable one’s analytics & AI journey.
- Career assistance via portfolio showcase and recruiter interface; Social Recognition and Personal Brand Creation.
Components of the Program
- Virtual
The program is delivered in an online format with a highly effective blend of weekend live sessions followed by self-paced practice sessions spanning across 10 months - Experiential
More than 50 industry standard data cases from across industries to be used for applied learning. - Collaborative
The LEAPS platform enables learners to collaborate on projects, helping them perform better in teams. - Academic/Industry Leaders Interactions
40 live sessions planned with Academic and Industry leaders for detailed and application-oriented learning. - Milestone based Learning
Entire curriculum is divided into three manageable learning blocks to help the learner reap benefits in an incremental manner. Separate certificate for each block, followed by a certificate for the overall program - Hackathons and Developing live Projects
The learner will get the opportunity to participate in live Hackathons and develop cutting edge ML solutions under the guidance of experts and implement those, with the opportunity to develop and showcase a portfolio of projects and solutions
Panel of Instructors
Tenured Academicians and Industry Practitioners
Learner Profile & Requirements
- Engineers / Associates / IT Professionals:
Software engineers in IT/ITES, Startup teams building ML products/services. - Managers:
Product managers, Program managers, General Managers, etc. interested in improving their analytical skills and effectively managing analytics, data science and machine learning projects. - Data Analysts / Business Analysts / Data Scientists:
Who want to transition to or progress into data science/ analytical roles and become more efficient and effective in data-driven decision-making. - Consultants:
Who are driving client projects and looking for acquiring and honing cutting edge analytical and data science skills for a career transition or progress
Eligibility:
- Bachelor’s Degree.
- Working professional with min 2 years of work experience.
No prior coding experience is needed.
Commitment for Hands-On/Lab effort is 8-12 hours per week throughout the program
Trainer/Mentor Role
Trainers/Mentors significantly contribute towards enhancing the value of the program by playing the below roles in every step of the learner’s journey throughout the program
- Conduct the live sessions:
- Academic sessions to explain concepts.
- Case Study sessions to demonstrate application of concepts.
- Mentor learners through their program journey:
- Help learners develop a learning path in Data Science and ML.
- Help learners build an effective ML project portfolio and guide them for the job interviews.
- Closely involved with designing, creating, curating, carefully reviewing, and enhancing the contents
Key Benefits from the Program
- Learn from basics of Data Science & Python Programming to Advanced ML.
- Build and Apply: Develop the ability to translate business problems to analytics & ML problems and develop impactful solutions.
- Interact with Academic Experts and Industry Practitioners and get mentored – Train on detailed concepts and the nuances of solutioning.
- Reduce learning curve and be Industry Ready: The in-built Analytics and AI functions of the platform will reduce the learning curve and help to focus more on solutioning rather than just programming.
- Dynamic Collaboration: The Collaboration features of the platform will enable to form teams, develop effective solutions and become better team players.
- Regular Hackathons: Compete & apply learnings under strict timelines and achieve tangible recognition/personal brand in the Data Science community.
- Build and showcase cutting edge ML solutions using the LEAPS platform, with long term access to learning content/platform.
- Industry standard assessment tests and long-term access to material will enable one’s Data Science journey, even after the program completion.
- Access to Recruiters: Analyttica’s partnership with India’s leading job portals/Recruiters will help you in position yourself well in the market.
Curriculum Details
Program Commencement (1 Week)
- Essence of the Platform
- Overview of the Program
- Usage of the LEAPS platform to develop content and/or case studies.
- Industry & Market Perspective
Sample Contextual Projects/Case Studies
Retail/Etail
- Market Basket Analysis in Retail - Application of Association Rule Mining Technique
- Customer Sentiment analysis for an e-commerce retailer
- Predict Holiday Sales for A Retail Client - Application of Linear Regression
Banking & Financial Services
- Analyze Credit Card Spend Data - Application Of Descriptive Analysis Techniques
- Identify risk class and eligibility of a customer : Application of Machine Learning
- Identify Customers with Higher Likelihood of Credit Card Attrition - Application of Decision Tree
- Build a Regression Tree for Predicting Spend on Credit Card
Healthcare
- Detection of Breast Cancer in A Clinical Trial - Application Of SVM
- COVID-19 Data Exploration & Visualization
Human Capital
- Application of Non-Hierarchical Clustering in HR Analytics Domain
- Recognizing human activity - An application of supervised machine learning
Manufacturing
- Estimating Price for Diamonds - Supervised Learning -Hyperparameter Optimization
- Predictive maintenance of equipment data - Building predictive models using sensor data
Sports
- Identify the Top Performing Players in a Domestic Cricket League - Application of Descriptive Analysis Techniques
- Fantasy Cricket Team Creation - Application of Linear Programming
Learning block 1
Foundations of Data Science and Machine Learning (15 Weeks)
- Introduction to Python
- Python for Data Science
- Data management and visualization using Python
- Exploratory Data Analysis
- Inferential Statistics
- Hypothesis Testing
- Data Preparation & Treatment
- Variable Transformation & Reduction
- Feature Scaling Techniques
- Feature Engineering Techniques
- Linear Regression
- Advanced Regression Techniques
- Logistic Regression
- Model selection techniques
- Model evaluation techniques
(2 Weeks with commitment of 10 hours per week)
(3 Weeks with commitment of 10 hours per week)
(3 Weeks with commitment of 10 hours per week)
(5 Weeks; 10 hours per week)
End of module assessment and Hackathon
Learning block 2
Machine Learning and Natural Language Processing (13 Weeks)
- Naïve Bayes Classifiers
- Support Vector Machine (SVM)
- Decision Trees
- Bagging & Random Forest Classifiers
- Boosting Algorithms – AdaBoost, Gradient Boost, XGBoost, CatBoost
- Hyperparameter Tuning – Logistic & Linear Regression
- Hyperparameter tuning in Tree based models
- Hyperparameter tuning in SVM models
- Introduction to Explainable AI (XAI) using LIME & SHAP (Optional)
- Hierarchical and Non-Hierarchical Clustering Techniques
- Introduction to Natural Language Processing
- Text Mining Techniques
- Basic Lexical Processing in Text Analytics
- Sentiment Analysis - Using Unstructured Text Data
- Introduction to Syntactic & Semantic Processing of Text Data
- Text Analytics - Classification and Clustering
- Explainable AI (XAI) on Text Data using LIME & SHAP (Optional)
(5 Weeks; Commitment of 10 hours/week)
(2 Weeks; Commitment of 10 hours/week)
(2 Weeks; Commitment of 10 hours/week)
End of module assessment and Hackathon
Learning block 3
Deep Learning, Industry Domains and Projects (12 Weeks)
- Basics of Neural Network
- Multilayer Perceptron Model – Classification & Regression
- Training & Evaluating Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Application of Data Science and Machine Learning in Banking & Financial Services
- Application of Data Science and Machine Learning in Retail
- Application of Data Science and Machine Learning in Manufacturing
- Choosing the final ML project to be executed
- Personalized mentorship for Career goals
- Resume development and feedback sessions
- Deploy the ML solutions built on a cloud-based platform as an e-portfolio
(4 Weeks; Commitment of 10 hours/week)
(3 Weeks; Commitment of 10 hours/week)
(5 Weeks; Commitment of 12 hours/week)
End of module assessment
Sample Certificate on Completion

REFERRAL PROGRAM
Refer a friend and *get an additional 10% discount on the fees
*Referral discount is applicable to both the parties only post enrolment.
About Analyttica
Contextual Analytics and AI Solutions Company
Analyttica Datalab Inc. is an Analytics and AI platform solutions company. With a strong focus on the EdTech space, Analyttica is determined to bridge the gap due to unavailability of experienced talent in analytics & AI.
Analyttica was founded in 2012 by global Analytics leaders, who have a world-class reputation and decades of experience in building contextual Data Analytics talent capabilities and solutions across 25+ countries for one of the largest multi-national banks in the world. Analyttica operates at the confluence of ‘Industry Experience’, ‘Analytical Expertise’, and ‘Technological Excellence’. Its simulation-based contextual approach is unique and innovative that thrives on a structured ‘Man-Machine’ blend for creating sustainable learning and robust business integrated solutions.
Essence behind our EdTech Focus
85% of the technology enabled companies globally have a data strategy; about 77% of them have implemented some form of analytics & AI tools/technologies. However, only 31% of them have been able to demonstrate RoI, and less than 13% have been able to operationalize analytics & AI into production.
There are three primary reasons for above:
- Lack of a powerful technology enabled environment enabling business purpose integration/contextualization.
- Process breakage mitigation that occurs in Value-Chains, Workflows, Planning, and Work Cross-Leverage.
- Lack of experienced talent with the right skills to apply and collaborate in a business contextual manner, with ability to institutionalize themselves.
Analyttica, with it’s innovative EdTech solution called Analyttica TreasureHunt® LEAPS for upskilling in the analytics and data-science space focusses on the third aspect of above to address the acute shortage of the right talent, with a strong organizational need across industry verticals to hire and onboard talent with the right education and practical skills to apply analytics & data science to create business impact.
The LEAPS Platform
Analyttica TreasureHunt® LEAPS is a holistic upskilling platform driven by a unique, cohesive “Learn-Apply-Solve” framework. This innovative solution provides application-oriented immersive and interactive learning experience with extensive real-industry courses, cases, datasets and projects. It also ensures a blended pathway between industry and academia through simulation and contextualisation.
The technology and design behind LEAPS, is patented in USA (Patent # 9,886,867 ) for invention in education/learning pertaining to analytics and data science applications.
LEAPS’ Training & Knowledge Immersion solution helps organizations and universities, and learners in the B2C space step-up in leveraging the benefits of analytics and data science applications contextually via an innovative platform/engine for experiential learning, gamification, and a holistic platform for upskilling and re-skilling.
Key features of LEAPS
- Innovative experiential learning
- Structured courses for industry readiness
- Ability to upload one’s own data and create projects
- Ability to convert real projects to simulations/ institutionalising all knowledge
- Innovative, personalised and gamified UX/UI
LEAPS Traction
- 200K+ Data Science enthusiasts on LEAPS (since launch in April 2020).
- One of the largest conglomerates in India uses LEAPS to address their Analytics Upskilling needs.
- Leading partner for the government of India backed ambitious skilling initiative called NASSCOM FutureSkills.
- Large Healthcare company in USA uses it for solutioning and learning.