Business analytics is the process of using data, statistical and quantitative analysis, and predictive modelling to make informed business decisions. It involves the use of various tools and techniques to analyze data and extract insights that can help businesses improve their operations, optimize their processes, and make better strategic decisions
Business analytics is important because it enables businesses to gain a deeper understanding of their operations, customers, and markets. By analysing data, businesses can identify patterns and trends that may not be immediately apparent, and use this information to make more informed decisions
Here are some reasons why business analysis has become a popular career choice:
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CURRICULUM
Induction and Introduction to Analytics
- Introduction to business analysis
- Business process modelling and improvement
- Data modelling and database concepts
- Types of Analytics: Descriptive, Diagnostic, Predictive and Prescriptive
- Data and Data Sources for Analytics Small Data, Big Data, Traditional Data, and Non-Traditional data Sources
- Analytics in Business
- Introduction to problem Solving using data
- Analytics tools: What tools to use for which type of Problems?
- Types of Error Data Analysis
Introduction to Python
- Overview of Python- Starting Python
- Introduction to Python Editors & IDE's (Jupiter, Ipython etc.…)
- Concept of Packages/Libraries - Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.)
- Data Types & Data objects/structures (Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
- Variable & Value Labels – Date & Time Values
Data Import/Data Export
- Importing Data from various sources (Csv, txt, excel, access etc)
- Viewing Data objects - subsetting, methods
- Exporting Data to various formats
Data Manipulations (Packages Pandas, NumPy, etc.)
- Cleansing Data with Python
- Data Manipulation steps (Sorting, filtering, duplicates, merging, appending,
- subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc.)
- Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)
- Python Built-in Functions (Text, numeric, date, utility functions)
Exploratory Analysis & Data Visualizations (Packages like Matplotlib, SciPy. Stats etc.)
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc.)
Fundamental of Statistics
- Types of Variables, measures of central tendency and dispersion
- Variable Distributions and Probability Distributions
- Normal Distribution and Properties
- Central Limit Theorem and Application
Basic Statistical Analysis
- Statistics Basics Introduction to Data Analytics, descriptive and summary
- Inferential statistics
Statistical Significant Tests
- Hypothesis Testing Null/Alternative Hypothesis formulation
- Z‐Test, T‐Test, Chi‐Square test
- Analysis of Variance (ANOVA)
- Chi Square Test
- Correlation Analysis
Data Preparation
- Need for data preparation
- Outlier treatment
- Missing values treatment
- Multicollinearity
Predictive modeling & Time Series Analysis
- Basics of regression analysis
- Linear regression
- Logistic regression
- Interpretation of results
- Multivariate Regression modeling
Machine Learning Algorithm
- Text Analytics
- Random Forest
- Support Vector Machine (SVM)
- Naïve Bayes Algorithm
- K-NN Classification & Regression