Business and Big Data Analytics
Decision Scientist – an opportunity to Brand your Brain Power, an opportunity to transform your career as a professional. A profession from an industry, which is expected to grow from USD 130 Billion (2016) to USD 203 Billion (2020) at a 11.7% CAGR. A compensation to claim from USD 100,000 to USD 250,000 where 1-in-4 Hiring Manager is on the lookout for a Decision Scientist and/or Analytics professional.
VARSA offers program on Business and Big Data Analytics, a program that encapsulates all aspects needed for the much sought after profession – Decision Scientist. A no frill, 100% classroom based long-term non-disruptive weekend program to meet the growing demands of data and related monetization.
With a right blend of scope, pedagogy and resource that includes Fundamentals, Machine and Deep Learning, the program offered by VARSA shall stand on the right side of the spectrum giving you the completeness and confidence to carve a niche for yourself in the field as Decision Scientist.
The course is accredited and certified by Analytics Society of India, fuelled with classroom sessions handled by passionate Decision Science experts from eminent institutes (Academia and Industry).
- Build conceptu-al and practical understanding of Analytical, Machine & Deep Learning techniques, Artificial and Cognitive intelligence in the ecosphere of Business and Big Data.
- Learn to use tools – R, Python, Tableau, Apache Spark, Amazon AWS & Analytics with MS Excel and build a brief understanding of tools like Hadoop, HDFS, MapReduce, PIG, HIVE, NoSQL applied in Big Data architecture.
- Understand sources of Big Data & build an ability to analyze high dimensional data(Structured/Unstructured) in certain & uncertain environments.
- Learn through Case Studies to apply analytic techniques & understand its competitive advantages over heuristic decision making in various functions of businesses like Human Resources, Finance, Supply Chain, Operations, Marketing, Sales, CRM & Management.
Key Features of the Course:
- E2E spectrum of decision science covering all facets of data through Classical, Bayesian, Stochastic and Machine / Deep Learning techniques.
- Eminent academicians and practitioners as course advisors and faculties.
- Program accredited and certified by Analytics Society of India.
- Course pedagogy comprises of assignments, hands on projects, presentation of projects in ASI symposium.
- Spans across 235 hours of in classroom knowledge sharing
- Non-disruptive weekend program
- Acts as a platform to connect with industry experts and get mentored – V Connect
- Route α – Your club to connect and progress.
Module 1 : Foundation of Decision Science
- Introduction to Decision Science, overview of tools, technologies and trends.
- Fundamentals of Classical Statistics.
- Fundamentals of Probability concepts and Bayesian Statistics.
- Process of data quality checks and methods to treat missing or sparse data.
- Data exploration and visualization.
- Building fundamentals and starting the journey with R and Python. Introduction to Rattle, Rmarkdown and Rshiny.
Tools: R, Python, MS Excel
Case Studies: Analytics in HR – Predicting Job Acceptance (IIMB Case)
Module 2: Predictive Analytics
- Simple and Multiple Linear Regression.
- Logistic & Multinomial Regression.
- Decision Trees – CHAID, CART, C5.0 boosting.
Tools: R, Python, MS Excel
Case Studies: Pricing of players in the Indian Premier League (IIMB Case), Breaking Barriers – Micro-Mortgage Analytics (IIMB Case)
V-Connect: Session on ‘Analytics in Marketing’ by Industry Guest
Module 3: Stochastic Analytics
- Behavioral analytics.
- Markov Models – Classification of Markov States, Brand switching and loyalty modeling.
- Survival analytics – Renewal and Queue theory, Warranty analytics – Poisson process.
- RFM analysis.
- Time Series, Moving Average, Exponential Smoothing, ARIMA, ARIMAX.
Tools: R, Python, MS Excel
Case Studies: Customer Analytics at Flipkart(IIMB Case), Warranty of white goods
V-Connect: Session on ‘Analytics in BFSI’ by Industry Guest
Module 4: Prescriptive Analytics
- Linear, Non Linear and Integer Programming – Formulating problem, interpreting results.
- Sensitivity analysis with application in operational scenario.
- Multi Criteria Decision making.
Tools: MS Excel
Case Studies: HAL Case Study (IIMB Case)
V-Connect: Session on ‘Analytics in Manufacturing’ by Industry Guest
Module 5: Big Data Analytics – Machine Learning
- Introduction to Big Data, Machine learning, Deep learning and Artificial Intelligence.
- Matrices, Eigen value & Eigen vectors.
- Introduction and working with AWS.
- Introduction to idea of bagging and boosting.
- Gradient & Stochastic gradient descent, Dynamic Programming.
- Supervised Learning: Naive Bayes Classifier, Support vector machine, K Nearest neighbor, Ridge,LASSO, Elastic Net.
- Ensemble Modeling: Random Forest, XGBoost, Adaboost, Gradient boosting machine, Customized ensemble modeling.
- Unsupervised Learning: K-Mean clustering, Principal Component Analysis, Pattern recognition.
- Reinforcement learning: Advanced recommender system, Collaborative filtering.
Tools: R, Python, AWS
Case Studies: Local transport aggregators, Pedigree vs Grit: Predicting Mutual Fund Manager Performance (Kellogg Case), Online Product basket recommendation (IIMB Case), Predict machine downtime with sensor data, Fraud Analytics – Manipulation of Financial numbers (IIMB Case)
V-Connect: Session on ‘Future trends of Decision Science and Opportunities’ by Industry Guest
Module 6: Big Data Analytics – Deep Learning, Artificial Intelligence & Cognitive Computing
- Neural networks – Single & multi Layer Perceptrons, Nodes (Input, hidden & output), activation function,filters, back-propagation, gradient descent, cost function, learning rate, dropout.
- Types of Neural networks : Convolution – VGG, ResNet, DeConvNets, Recurrent – LSTM
- Deep Learning in Image processing.
- Image classification, Semantic Segmentation, Object detection, Medical Images, Art generation, Videos.
- Deep Learning in Natural language processing.
- Text Mining, Word2Vec, Semantic mapping, CBOW, N-Grams, T-SNE, seq2seq models, Semantics.
- Sentiment Analysis, Document Clustering, Chat-bots, Language Translations.
- Artificial Intelligence and Cognitive Computing.
- Introduction and difference between Artificial Intelligence and Cognitive Computing
- Process of Artificial Intelligence, Cognitive computing and Robotic automation using deep learning.
- Intelligent agents, Heuristic Algorithms, Markov decision Process, Reinforcement learning, Pattern recognition
- Deploying Artificial Intelligent algorithms
- Developing Cognitive computing systems embedded with NLP and Artificial Intelligence
Tools: R, Python, AWS.
Case Studies: Satellite Images, Document Clusters, Youtube Videos, Online reviews, Chatbots
V-Connect: Session on ‘Analytics in Social Sector and Healthcare’ by Industry Guest
Module 7: Big Data Ecosphere
- Understanding Big Data architecture.
- Overview of Cloud computing platforms for Big Data analysis,using Google Cloud, Amazon AWS and Microsoft Azure.
- Overview of Hadoop and MapReduce ecosystem: Storing and managing large-scale structured and unstructured data.
- Overview of large scale data ingestion using Sqoop, Flume, NoSQL.
- R libraries for graph processing.
- MLIB (Machine Learning Library): How to use APIs to develop models.
- Apache Spark.
- Spark Architecture – Deep Dive.
- Spark APIs & Usages.
- Working with Advanced Spark Features.
- Writing Spark Streaming Applications.
- Using Spark Machine Learning Algorithms.
- Amazon Web Services.
- Getting familiar with AWS and its Components.
- Big Data Streaming and Amazon Kinesis.
- Big Data Processing and Analytics
- Deploying a Machine learning Solution in AWS..
- Tableau Features.
- Tableau APIs, Dynamic Data Manipulation and Dashboards.
- Complex data exploration and visualizations using Tableau.
Module 8: Social and Digital Media Analytics
- Social Media and Digital Metrics.
- Process of Sweeping content from various online platforms like Facebook, Twitter, Youtube, Instagram, Websites, Discussion forums.
- Data gathering from online Portals and Banners.
- API for real time feeds and analyzing the textual data on the run.
- Social network analysis.
- Analyzing Social media and Digital data.
- Search Engine Optimization.
- Campaign effectiveness in Digital Media Advertising.
Tools: R, Python, AWS.
Case Studies: 1920 Evil Returns – Bollywood and Social Media Marketing, Retail and Finance Industry
V-Connect: Session on ‘Analytics in Entertainment’ by Industry Guest
Module 9: Data ethics, IP Protection, Story telling and Data security
- Business Story Telling – Decision Science approach.
- Ethical standards of a Decision Scientist.
- IP Protection and Process.
- Data protection and Security.
A well drafted program design should infuse the optimal blend of essential aspects of learning. At VARSA,the course design is formulated not only with a view of efficient deliverables, but also to evaluate the progress of learning. Sessions shall be delivered through experienced (100% hands-on resources) as Data Science is well learnt through practitioners rather than pure academicians. The program is blended with Industry Connect, Hands-on, Case Studies, In-classroom, Peer-Peer, Continuous Assessments and External Validation is earmarked to deliver that unique experience of right knowledge augmentation with very early Return on Investment.
Who Should Attend:
The Business and Big Data Analytics will equip the participants with analytical tools and prepare them for corporate roles in analytics based consulting in Marketing, Operations, Supply Chain Management, Finance, Insurance and General Management in various industries, Course is suitable for those who are already working in Analytics to enhance their knowledge as well as for those with analytical aptitude and would like to start a new career in analytics.
|Module||Session Dates (Year–2018)|
|SPL||Introduction to working with R||20 Jan|
|1||Foundation of Decision Science, Working with R / Python||21 Jan, 27 – 28 Jan, 3 Feb|
|SPL||Introduction to working with Python||4 Feb|
|2||Predictive Analytics||24 – 25 Feb, 3 – 4 Mar|
|3||Stochastic Analytics||24 – 25 Mar, 7 Apr|
|4||Prescriptive Analytics||28 – 29 Apr|
|5||Big Data Analytics – Machine Learning||11 – 13 May, 19–20 May, 26 May|
|6||Social and Digital Media Analytics||22 – 24 June|
|7||Data Ethics, IP Protection, Story Telling and Data Security||24-Jun|
|8||Big Data Ecosphere||7 – 8 Jul, 14 Jul|
|9||Big Data Analytics – Deep Learning, Artificial Intelligence & Cognitive Computing||27 – 29 Jul, 4 – 5 Aug,
11 – 12 Aug
Program Fee: INR 1,39,000 (One Lakh Thirty Nine Thousand) + applicable taxes.
20% Sponsorship for Group Registration (3 or more)
Payment: Post Dated Cheques.
Award of Certificate:
A certificate of completion will be awarded through Analytics Society of India to the participant at the end of the program upon successful completion of the program satisfying the program requirements.