Advanced Data Science for Practicing Business Analysts
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Necessity and success of use of analytics have led to the creation of analytics cell in most organizations dealing with data driven decision making. The subject being relatively new, there is acute shortage of formally qualified professionals for this field. Consequently, many of these working professionals have acquired their skills by on the job trial and error experience using software tools available in the organizations without any formal exposure to this complex subject. This restricts their understanding of the subject, often preventing them from utilizing the full potential of modern statistical and machine learning tools and techniques. A formal exposure to the theories and principles of the underlying methods they use would have significantly enhanced the quality of solutions they would otherwise deliver. Familiarity with emerging methods, tools and techniques beyond the limited options they are exploring would also add to their productivity. It will be an opportunity for such a group of professionals to familiarize themselves with state of the art theories and principles of analytics in action.
The main objectives of this five day executive development programme are:
- To understand and appreciate the underlying theoretical issues that govern applicability, success, and failure of statistical and machine learning models in decision making
- To go deep into the widely used statistical and machine learning methods useful in solving data driven business decision problems in organizations
- Art of Data Cleaning: Outlier management, data imputation and influence diagnostics
- The challenge of variable selection in a model: Dimensionality reduction using Principal Component Analysis and other methods
- Study of qualitative factors in experiments: Categorical Data Analysis
- Artificial Neural Network and Deep Learning in predictive analytics: Finer issues and parameter setting
- Challenges in unsupervised learning: Quality evaluation of clusters
- Building statistical models in business planning: Forecasting techniques
- Integrating Artificial Intelligence with human expertise: Bayesian Techniques
- Challenges in Network Analytics: Modeling of network effect in organizational strategies
- Decision making from sample surveys: Determination of Sample Size and choice of sampling
Interactive class room learning with lectures. Case based experiential learning with participants
Who should attend
Target group of participants and prerequisites: This programme is aimed at working professionals dealing with Data Science and analytics based model building. Participants should have prior exposure on the basics of statistical and machine learning principles. Participants must have inclinations toward mathematics, statistics and algorithmic problem solving. They must also have keen interest in understanding the conceptual intricacies of popular methods used in analytics.