Advanced Certification in Data Analytics for Business

Learn how to do data analysis, data transformation and other important data analysis functions. Basic fundamental of Python and learn how to do data analytics using the same.

2.1 Introduction to SQL
2.2 Database normalization
2.3 Entity-relationship model
2.4 SQL operators
2.5 Join, tables, and variables
2.6 SQL functions
2.7 Subqueries
2.8 SQL functions, views, and stored procedures
2.9 User-defined functions
2.10 SQL performance and optimization
2.11 Advanced concepts

• Correlated subquery
• Grouping sets

3.1 What is Data Science
Significance of Data Science in today’s data-driven world, its applications of the lifecycle, and its components Introduction to R programming and RStudio

Hands-on Exercise:
1. Installation of RStudio
2. Implementing simple mathematical operations and logic using R operators, loops, if statements, and switch cases

3.2 Data Exploration

• Introduction to data exploration
• Importing and exporting data to/from external sources
• What are data exploratory analysis and data importing?
• DataFrames, working with them, accessing individual elements, vectors, factors, operators, in-built functions, conditional and looping statements, user-defined functions, and data types

Hands-on Exercise:
1. Accessing individual elements of customer churn data
2. Modifying and extracting results from the dataset using user-defined functions in R

3.3 Data Manipulation

• Need for data manipulation
• Introduction to the dplyr package
• Selecting one or more columns with select(), filtering records on the basis of a condition with filter(), adding new columns with mutate(), sampling, and counting
• Combining different functions with the pipe operator and implementing SQL

Hands-on Exercise:
1. Implementing dplyr
2. Performing various operations for manipulating data and storing it

4.1 Descriptive Statistics

• Mean,
• Median,
• Mode,
• Tables,
• Charts

4.2 Introduction to Probability

• Probability in Business Analytics
• Probability Distributions
• Binomial distribution
• Poisson distribution
• Normal distribution
• Hypothesis Testing and Estimation
• Hypothesis testing
• Estimation

Hands-on Exercise:
1. Building a statistical analysis model that uses quantification, representations, and experimental data
2. Reviewing, analyzing, and drawing conclusions from the data

5.1 Business domains

• Finance
• Marketing
• Retail
• Supply Chain

5.2 Understanding the business problem and formulating hypotheses
5.3 Exploratory data analysis
5.4 Data storytelling: Narrate stories in a memorable way
5.5 Project on deriving business insights and storytelling

6.1. Regression

1. linear & logistics
2. EDA
3. Handling unbalanced data
4. Dimension reduction techniques
5. Model validation
6. Bias variance trade-off”

6.2. Trees (Decision & Random Forest)

1. Modeling the relationship within data using linear predictor functions
2. Implementing linear and logistics regression in R by building a model with ‘tenure’ as the dependent variable
3. Implementing predictive analytics by describing data
4. Explaining the relationship between one dependent binary variable and one or more binary variables
5. Using glm() to build a model, with ‘Churn’ as the dependent variable
6. Implementing random forest for both regression and classification problems
7. Building a tree, pruning it using ‘churn’ as the dependent variable, and building a random forest with the right number of trees
8. Using ROCR for performance metrics
9. k means clustering, Applications of Unsupervised (Market Basket Analysis, Segmentation)

Hands-on:

1. Deploying unsupervised learning with R to achieve clustering and dimensionality reduction
2. K-means clustering for visualizing and interpreting results for the customer churn data
3. Hyperparameter optimization
4. Handling Unstructured Data
5. Deploying association analysis as a rule-based Machine Learning method
6. Identifying strong rules discovered in databases with measures based on interesting discoveries

7.1 Introduction to KNIME
7.2 Working with data in KNIME
7.3 Loops in KNiME
7.4 Webscraping in KNIME
7.5 Hyperparameter optimization in KNIME
7.6 Hyperparameter optimization for Machine Learning Models using loops in KNIME
7.7 Feature Selection in KNIME

8.1 Programming with R
8.2 Advance Statistics

• ANOVA
• Regression analysis
• Dimension reduction techniques

8.3 Data Mining

• Supervised and unsupervised learning
• Clustering
• Market Basket Analysis
• Decision trees
• Random forest
• Neural networks

9.1 Multiple linear regression
9.2 Logistic regression
9.3 Linear discriminant analysis

10.1 Introduction to time-series
10.2 Correlation
10.3 Forecasting
10.4 Autoregressive models

11.1 Handling unstructured data
11.2 Machine Learning algorithms
11.3 Bias variance trade-off
11.4 Handling unbalanced data
11.5 Boosting
11.6 Model validation

12.1 Hyper parameter optimization
12.2 Advance Machine Learning Libraries – XGBoost
12.3 Solving Problems on Kaggle

13.1 Framework to Data Science strategy
13.2 Mapping Data Science with data architecture strategy
13.3 Executing Data Science strategy

14.1 Marketing & retail analytics
14.2 Social analytics
14.3 Logistics & supply chain
14.4 Financial analytics

15.1 Introduction to Power BI
15.2 Data Extraction
15.3 Data Transformation – Shaping & Combining Data
15.4 Data Modelling & DAX
15.5 Data Visualisation with analytics
15.6 Power BI Service (Cloud), Q&A, and Data Insights
15.7 Power BI Settings, Administration & Direct Connectivity
15.8 Embedded Power BI with API & Power BI
15.9 Power BI Advance & Power BI Premium

16.1 Introduction to Presto
16.2 Writing Queries in Presto on large data sets.
16.3 Data Transformation using Presto

In the Data Science & Business Analytics Capstone project, you will use all the knowledge and skills you have acquired throughout this advanced certification program and get real world Industry project exposure.