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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:

  • High Demand: Business analysts are in high demand as organizations strive to become more data-driven and efficient. Businesses need professionals who can analyze data, identify insights, and make recommendations to drive growth.
  • Versatile Skills: Business analysts require a combination of technical, analytical, and business skills. They must be able to understand business processes, communicate with stakeholders, and use data to inform decisions. These versatile skills make business analysts valuable across a variety of industries.
  • Career Growth: Business analysis is a dynamic field that offers opportunities for career growth. As you gain experience and skills, you can progress to more senior roles and take on more responsibility.
  • Competitive Salaries: : Business analysis is a well-paying profession, with competitive salaries and benefits packages. According to Glassdoor, the average salary for a business analyst in the US is around $75,000 per year, while senior business analysts can earn upwards of $100,000.
  • Impactful Work: : Business analysts can have a significant impact on businesses, helping them make more informed decisions, identify new opportunities, and optimize their operations. As a business analyst, you can play a crucial role in driving the success of a business

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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

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