Skip to content
CampusPrep
AD-404 · DATA SCIENCE/Official Syllabus

DATA SCIENCE (AD-404) - Official Syllabus

1
Unit 1

Introduction to Data Science

  • Types of Data: structured and unstructured data
  • Data Science Road Map: Frame the Problem
  • Understand the Data
  • Data Wrangling
  • Exploratory Analysis
  • Extract Features
  • Model and Deploy Code
  • Graphical Summaries of Data: Pie Chart
  • Bar Graph
  • Pareto Chart
  • Histogram
  • Measures of central tendency of Quantitative Data: Mean
  • Median
  • Mode
  • Measures of Variability of Quantitative Data: Range
  • Standard Deviation and Variance
  • Probability: Introduction to Probability
  • Conditional Probability
2
Unit 2

Unstructured Data Analytics

  • Importance of Unstructured Data
  • Unstructured Data Analytics: Descriptive, diagnostic, predictive and prescriptive data Analytics based on Case study
  • Data Visualization: boxplots
  • histograms
  • scatterplots
  • features map visualization
  • t-SNE
  • Overview of Advance Excel- Introduction
  • Data validation
  • Introduction to charts
  • pivot table
  • Scenario manager
  • Protecting data
  • Excel minor
  • Introduction to macros
3
Unit 3

Statistical & Probabilistic analysis of Data

  • Multiple hypothesis testing
  • Parameter Estimation methods
  • Confidence intervals
  • Correlation & Regression analysis
  • logistic regression
  • Shrinkage Methods
  • Lasso Regression
  • Bayesian statistics
  • L1 and L2 regularizations
  • POWERFUL DATA ANALYSIS—SUMIFS
  • SUMPRODUCT
  • VLOOKUP | XLOOKUP
  • INDEX + MATCH
  • Handling Formula Errors
  • Dynamic Array Formulas
  • Circular References
  • Formula Auditing
  • Pivoting
4
Unit 4

Data Manipulation With Pandas

  • Introduction to Pandas
  • understanding DataFrame
  • Missing Values
  • Data operation
  • String Manipulation
  • Regular Expressions and Data learning
  • Outlier and Error
  • Visualization tool in Python: Representation of Pie Chart, Bar Chart, Histogram, Scatterplots using Python
  • Data Analysis
  • performance metrics
  • ROC curve
  • types of errors
  • Overfitting & Under fitting
  • evaluating performance of learning model: Holdout
  • Random sampling
  • cross validation and Bootstrap method
  • Bagging & boosting
  • Gradient Boosting
  • Random Forests
  • Committee Machines
5
Unit 5

Introduction to Business Intelligence

  • Introduction
  • Types of Business Intelligence
  • Modern Business Intelligence Tools
  • Modern Business Intelligence
  • Data Science and Ethical Issues- Unfair discrimination
  • Reinforcing human biases
  • Lack of transparency
  • Discussions on privacy, security, ethics
  • Role of Next-generation data scientists

Reference Books

  • The Data Science Workshop — Anthony So, Thomas V. Joseph, Robert Thas John, Andrew Worsley, Dr. Samuel Asare — Packt Publication
  • Python Data Science Handbook — Jake VanderPlas — OREILLY
  • The Data Science HandBook — Wiley Publication
  • Principles of Data Science — Packt Publication
  • Microsoft Excel 2019: Data Analysis & Business Modelling — L. Winston Wayne — PHI
  • Data Collection: Methods, Ethical Issues & Future Directions (Government Procedures and Operations: Ethical Issues in the 21st Century) — Susan Elswick — Nova Science Publishers Inc
Go to where you left off?

Quick Add to Notes

Save questions, your own notes and screenshots into notes filed by unit. It takes a free account.

Create free account

Have an account? Log in