Certificate Course In Data Science

Unlock the World of Data with our Certificate Course in Data Science at DataVindyan

Are you eager to dive into the dynamic realm of data science? Look no further! Enroll in our comprehensive Certificate Course in Data Science at DataVindyan and embark on a journey of discovery and mastery. This program equips you with the skills and knowledge needed to extract valuable insights from data, make informed decisions, and drive innovation.

Our Certificate Course in Data Science offers a hands-on approach to learning, covering fundamental concepts such as data analysis, machine learning, and data visualization. Delve into real-world case studies, work with industry-standard tools, and develop a portfolio that showcases your expertise to potential employers. By the end of the course, you’ll be equipped to tackle complex data challenges and excel in the exciting field of data science.

Elevate your career prospects and stand out in a competitive job market with a recognized Certificate in Data Science from DataVindyan. Join us today and become part of a vibrant community of data enthusiasts and professionals.

Course Content

Mathematics For Data Science

Linear Algebra: Vector space, subspaces, linear dependence and independence of vectors, matrices, projection matrix, orthogonal matrix, idempotent matrix, partition matrix and their properties, quadratic forms, systems of linear equations and solutions; Gaussian elimination, eigenvalues and eigenvectors, determinant, rank, nullity, projections, LU decomposition, singular value decomposition.

Calculus and Optimization: Functions of a single variable, limit, continuity and differentiability, Taylor series, maxima and minima, optimization involving a single variable.

Statistics For Data Science

Probability and Statistics: Counting (permutation and combinations), probability axioms, Sample space, events, independent events, mutually exclusive events, marginal, conditional and joint probability, Bayes Theorem, conditional expectation and variance, mean, median, mode and standard deviation, correlation, and covariance, random variables, discrete random variables and probability mass functions, uniform, Bernoulli, binomial distribution, Continuous random variables and probability distribution function, uniform, exponential, Poisson, normal, standard normal, t-distribution, chi-squared distributions, cumulative distribution function, Conditional PDF, Central limit theorem, confidence interval, z-test, t-test, chi-squared test.

Computational Thinking

Variables, Initialization, Iterators, Filtering, Datatypes, Flowcharts, Sanity of data

Iteration, Filtering, Selection, Pseudocode, Finding max and min, AND operator

Multiple iterations (non-nested), Three prizes problem, Procedures, Parameters, Side effects, OR operator

Nested iterations, Birthday paradox, Binning

Introduction To Python

Module 1: Getting Started with Python

  • Introduction to Python and its features
  • Installing Python and setting up the development environment
  • Writing and executing your first Python program
  • Basic Python syntax and conventions

Module 2: Variables, Data Types, and Operators

  • Understanding variables and naming conventions
  • Common data types: integers, floats, strings, booleans
  • Type conversion and casting
  • Arithmetic, comparison, and logical operators

Module 3: Control Flow and Conditional Statements

  • Conditional statements: if, elif, else
  • Comparison operators and boolean expressions
  • Using logical operators (and, or, not)
  • Nested conditions and code blocks

Module 4: Loops and Iteration

  • Using for loops to iterate over sequences
  • The range() function
  • Using while loops for conditional iteration
  • Loop control statements: break and continue

Module 5: Lists, Tuples, and Dictionaries

  • Creating and manipulating lists
  • Accessing list elements using indexing
  • Working with tuples and their immutability
  • Introducing dictionaries and key-value pairs

Module 6: Functions and Modules

  • Defining and calling functions
  • Parameters and arguments
  • Return values and scope
  • Introduction to Python modules and libraries

Module 7: Strings and String Manipulation

  • Creating and formatting strings
  • String methods: concatenation, slicing, length
  • String interpolation and f-strings
  • Common string manipulation tasks

Module 8: File Handling

  • Reading from and writing to text files
  • Using context managers (with statement)
  • File modes: read, write, append
  • Handling exceptions in file operations

Module 9: Exception Handling

  • Understanding exceptions and error types
  • The try-except block for handling exceptions
  • Using else and finally clauses
  • Creating custom exceptions
Python for Data Science

Module 1: Introduction to Data Science and Python

  • What is data science and its importance
  • Role of Python in data science
  • Installing Python and essential libraries (Anaconda distribution)
  • Using Jupyter notebooks for data analysis

Module 2: Python Basics for Data Science

  • Variables, data types, and type conversion
  • Operators and expressions
  • Conditional statements and loops in data analysis context
  • Functions and their significance in data science

Module 3: NumPy: Numerical Computing with Python

  • Introduction to NumPy and its advantages
  • Creating arrays and working with array operations
  • Array indexing and slicing
  • Basic mathematical operations and broadcasting

Module 4: Pandas: Data Manipulation and Analysis

  • Introduction to Pandas and its key data structures (Series, DataFrame)
  • Loading data from various sources (CSV, Excel, SQL)
  • Data exploration, cleaning, and transformation
  • Filtering, grouping, and aggregation of data

Module 5: Data Visualization with Matplotlib and Seaborn

  • Introduction to data visualization and its importance
  • Creating basic plots with Matplotlib
  • Using Seaborn for more complex visualizations
  • Customizing plots and adding annotations
Basic of Database Management System (DBMS)

Module 1: Introduction to Databases and DBMS

  • Understanding the role of databases in data science
  • What is a Database Management System (DBMS)?
  • Types of databases: relational, NoSQL, NewSQL
  • Overview of popular DBMS options (MySQL, PostgreSQL, MongoDB, etc.)

Module 2: Relational Databases and SQL Basics

  • Introduction to relational databases and tables
  • Key concepts: tables, rows, columns, primary keys, foreign keys
  • Basic SQL queries: SELECT, FROM, WHERE, ORDER BY
  • Retrieving and manipulating data using SQL

Module 3: Data Modeling and Database Design

  • Entity-Relationship (ER) modeling
  • Creating ER diagrams to represent relationships
  • Converting ER diagrams to relational schemas
  • Normalization and denormalization techniques
SQL for Data Science

Module 1: Introduction to SQL and Relational Databases

  • What is SQL and its importance in data science
  • Introduction to relational databases and their structure
  • Key concepts: tables, rows, columns, primary keys, foreign keys

Module 2: Basic SQL Queries

  • SELECT statement and its syntax
  • Retrieving specific columns and all columns
  • Filtering data using the WHERE clause
  • Sorting data with the ORDER BY clause

Module 3: Filtering and Advanced Querying

  • Comparison operators (>, <, =, BETWEEN, LIKE)
  • Logical operators (AND, OR, NOT)
  • Working with NULL values
  • Using aggregate functions (COUNT, SUM, AVG, MAX, MIN)

Module 4: Joins and Relationships

  • Self-joins and multiple joins
  • Working with related tables
  • Handling duplicate rows with DISTINCT

Module 5: Grouping and Aggregation

  • GROUP BY clause and its role in aggregating data
  • HAVING clause for filtering aggregated results
  • Combining aggregate functions with GROUP BY
  • Creating calculated fields with aliases

Module 6: Subqueries and Nested Queries

  • Using subqueries to retrieve data from multiple tables
  • Correlated subqueries and non-correlated subqueries
  • Subqueries in SELECT, WHERE, and FROM clauses

Module 7: Data Modification and Transactions

  • INSERT, UPDATE, DELETE statements
  • Transaction management and ACID properties
  • Committing and rolling back transactions

Module 8: Working with Dates and Times

  • Date and time data types
  • Extracting information from dates (YEAR, MONTH, DAY)
  • Date calculations and formatting
Machine Learning

Machine Learning: (i) Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias-variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross- validation, multi-layer perceptron, feed-forward neural network; (ii) Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple- linkage, dimensionality reduction, principal component analysis.

Hybrid Mode

  • Live Classes
  • Get recording after the class
  • 1:1 Doubt clearing session
  • All the course materials are covered
  • Offline Classes on Weekends (Amravati Only)
  • Online Classes on Weekdays

Online Mode

  • Live Classes
  • Get recording after the class
  • 1:1 Doubt clearing session
  • All the course materials are covered
  • Online Classes

About Instructor:

Piyush Wairale, is currently working as an Instructor at IIT Madras BS in Data Science Degree. He completed his B.E from Prof. Ram Meghe Institute of Technology, Amravati and M.Tech from IIT Madras. He has 3 years of experience in online teaching and part of Education Data Mining International Conference 2023 held at IISc Banglore.

He is Google Certified Educator and Microsoft Azure certification educator at IIT Madras BS degree.

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Watch our Instructors sessions taken at IIT Madras Data Science Degree

DBMS Tutorial 

Mathematics for Data Science course session

Mathematics for Data Science Course session