Duration
3 days
Overview
Data analyst has just been rated one of the most demanding jobs of today, with companies rewarding skilled assets handsomely.
Our Data Analytics for Practitioner (DAP) course offers a full understanding of how data-driven models can improve your ability to make decisions in a fast-paced and uncertain world, and the ability to use modelling to predict future trends.
In our 3-days instructor-led course, you will be equipped with the right expertise to optimize the efficiency of any business, from operations to end-consumer goals, all with the data-driven insights you are entrained to obtain.
What you will gain:
- Incorporate text analytics over corresponding areas of commercial and retail production.
- Enhance the efficiency of customer behavioural modeling.
- Strengthen understanding in Text Analytics and Data Science.
- Grow the value-add of operations for higher management.
Day 1
The Business Case for Data Analytics
- Predicting Customer Behavior With Greater Accuracy and Precision
- Cost Improvements By Avoiding Manual Data Processing
- Innovation Improvements
- Analytics Maturity Curve
Data Analytics
- Introduction to Data Analysis
- Data Processing
- Data Quality
- Working With SQL vs NoSQL
Case Study
- Product Recommendation and Churn Modeling Cases Will Be Discussed
Hands-on Exercise
- Participants Will Be Asked to Assess Their Analytics Maturity
- Participants Will Be Asked to Define Their Problem Statements for Their Business Segments
- Participants Will Be Asked to Discuss Organizational and Cultural Limitations Within Their Companies to Broad Analytics Adoption
Day 2
Exploratory Data Analysis
- Distributions – Binomial, Power Law (Benford’s Law and Zipf’s Law), Normal, Etc.
- Descriptive Statistics
- Fitting Linear Models With lm
- Fitting Linear Mdels With Multiple Predictors
- Overfitting
Predictive Analytics With Python & R
- What is a Prediction?
- Sampling, Training, and Test Sets
- Constructing a Decision Tree for Purchasing Decision
- Collaborative Filtering for Recommendation Analysis
- Association Rule Mining for Recommendation Analysis
- Logistic Regression for Churn Modeling
- Neural Network for Churn Modeling
Customer Segmentation With Python and R
- Clustering Using K-means and Other Techniques
- Classification Using Naïve Bayesian and Random Forest
- Identifying Potential Customers
Hands-on Exercise
- Participants Will Be Asked to Perform Customer Segmentation on a Customer Data Set
- Participants Will Be Asked to Create a Recommender on a Customer/Product Data Set
Day 3
Data Visualization With Tableau
- Presenting Data
- Creating Dashboards and Storyboards
- Identifying Measures
Setting Up a Data Architecture
- Data Ingestion Tools – Nifi, Kylo
- Data Lakes – Hadoop & S3
- Data Transformation and ELT
- Data Storage (SQL vs NoSQL)
- Message Storage (Redis Pub-Sub)
- Streaming Data Storage and Processing (Spark)
- Search Database (ElasticSearch)
- Graph Database (Neo4J)
Hands-on Exercise
- Participants Will Be Asked to Create a Simple Dashboard in Tableau
- Participants Will Be Asked to Identify Roles and Responsibilities That are Either Lacking or Not Adequately Skilled for Data Analytics
Exam
Programme scope, delivery arrangements and suitability are confirmed during enquiry.