Our Course

DATA SCIENCE

ABOUT THIS COURSE

Course Description: Data Science

Data Science is an interdisciplinary field that leverages statistical methods, algorithms, and technology to extract insights and knowledge from structured and unstructured data. In today’s data-driven world, the significance of Data Science cannot be overstated. It empowers organizations across various industriesโ€”such as healthcare, finance, marketing, and technologyโ€”to make informed decisions, optimize processes, and innovate solutions.

THE COURSE INCLUDE

CURRICULUM

  • Overview of Data Science ๐ŸŒ
  • Key Roles & Responsibilities ๐Ÿง‘โ€๐Ÿ’ผ
  • Essential Tools & Technologies ๐Ÿ› ๏ธ.
  • Career Path in Data Science Fields ๐Ÿค‘
  • Roles in Data Science Field.

Module 1: Exploring Data ๐Ÿ”๐Ÿ“Š

  • Welcome to Excel: An Overview of the Worldโ€™s Most Popular Spreadsheet Software ๐Ÿ–ฅ๏ธ๐Ÿ“ˆ
  • Excel Beyond Limits ๐Ÿš€๐Ÿ“Š
  • Mastering the Basics of Spreadsheet Navigation ๐Ÿงญ๐Ÿ“‹
  • Working on Rows and Columns ๐Ÿ“๐Ÿ”ข
  • Looking for Exact Matches ๐Ÿ”โœ”๏ธ
  • Trimming Function and Its Usage โœ‚๏ธ๐Ÿ“‰
  • Sorting the Data ๐Ÿ”ข๐Ÿ”„
  • Nesting Functions ๐Ÿ—๏ธ๐Ÿ”ง
  • Data Types in Excel ๐Ÿ“Š๐Ÿ” 
  • Type Conversion in Excel ๐Ÿ”„๐Ÿ”ข

Module 2: Preparing Data ๐Ÿ› ๏ธ๐Ÿ“‹

  • Mastering Text Functions in Excel: CONCATENATE, UPPER, LOWER, and PROPER โœ๏ธ๐Ÿ”ค
  • Mastering Text Extraction in Excel: LEFT, RIGHT, and SUBSTITUTE Functions โœ‚๏ธ๐Ÿ”
  • Mastering Excelโ€™s DATE Function: Effective Techniques for Date Handling ๐Ÿ“…๐Ÿ› ๏ธ
  • Excel DATEDIF Function (Between Two Dates) ๐Ÿ“…โ†”๏ธ๐Ÿ“…
  • How To Use Relative & Absolute Cell References in Excel ๐Ÿ”„๐Ÿ”’
  • Harnessing the Power of VLOOKUP for In-depth Insights ๐Ÿ”๐Ÿ”Ž
  • Unleashing the Power of SUMIF Function in Excel ๐Ÿ”โž•

Module 3: Analyzing the Data ๐Ÿ“ˆ๐Ÿ”

  • Mastering the COUNT, COUNTA, and COUNTBLANK Functions ๐Ÿงฎ๐Ÿ”ข๐Ÿ“Š
  • Unleashing the Power of COUNTIF Function in Excel ๐Ÿ“Š๐Ÿ”
  • Using Excelโ€™s Core Calculation Functions: A Practical Guide ๐Ÿ“ˆ๐Ÿ› ๏ธ
  • Excel Logic Functions Explained: IF, AND, and OR in Practice ๐Ÿค”๐Ÿ”„
  • Organizing Data with UNIQUE and SORT Functions ๐Ÿ—‚๏ธ๐Ÿ”„

Module 1: SQL Fundamentals ๐Ÿ“š๐Ÿ’ป

  • SQL Installation & Setup ๐Ÿ› ๏ธ๐Ÿ”ง
  • Types of SQL Commands ๐Ÿ“œ๐Ÿ—‚๏ธ
  • DDL Commands for Database and Tables ๐Ÿ—๏ธ๐Ÿ“‹
  • SQL Constraints ๐Ÿšซ๐Ÿ“Š

Module 2: Data Manipulation with SQL (DML) โœ๏ธ๐Ÿ”„

  • Modifying Data in Tables โœ๏ธ๐Ÿ“‹
  • Retrieving Data with SQL ๐Ÿ”๐Ÿ“Š

Module 3: Intermediate SQL Queries ๐Ÿ”๐Ÿ“ˆ

  • Selecting Columns ๐Ÿ“‹๐Ÿ”ข
  • Filtering Rows ๐Ÿ”๐Ÿ—‚๏ธ
  • Aggregate Functions ๐Ÿ“Šโž•
  • Sorting & Grouping ๐Ÿ”ข๐Ÿ“Š
  • Null Values โ“๐Ÿ“‰
  • Date and Time Functions ๐Ÿ“…โฐ
  • Working with Expressions ๐Ÿงฎ๐Ÿ”ข
  • Order of Execution of SQL Commands ๐Ÿ”„๐Ÿ—‚๏ธ

Module 4: Joining & Combining Data ๐Ÿ”—๐Ÿ”„

  • Types of Joins ๐Ÿ”๐Ÿ”—
  • Left & Right Join โฌ…๏ธโžก๏ธ
  • Inner Join and Full Join ๐Ÿ”„๐Ÿ”
  • Cross Join and Self Join ๐Ÿ”„๐Ÿ‘ค
  • Understanding Table Relationships ๐Ÿ—‚๏ธ๐Ÿ”—

Module 5: Data Preprocessing & Analysis ๐Ÿ”ง๐Ÿ“Š

  • Handling Missing Values โ“๐Ÿ“‰
  • Handling Duplicates ๐Ÿ”„๐Ÿšซ
  • Data Transformation ๐Ÿ”„๐Ÿ› ๏ธ
  • Working with Dates and Times ๐Ÿ“…โณ
  • Data Filtering and Selection ๐Ÿ”๐Ÿ“‹
  • Analyzing Time Series Data ๐Ÿ“ˆโณ
  • Performance Optimization โšก๐Ÿ”ง
  • Working with JSON and XML Data ๐Ÿ“„๐Ÿ”ข
  • Data Blending ๐Ÿฅค๐Ÿ”„

Module 6: Advanced SQL ๐Ÿš€๐Ÿ“Š

  • SQL Views ๐Ÿ‘๏ธ๐Ÿ“‹
  • Triggers ๐Ÿšจ๐Ÿ”„
  • Performance Tuning โš™๏ธ๐Ÿš€
  • Backup and Recovery ๐Ÿ’พ๐Ÿ”„
  • Advanced Joins ๐Ÿ”—๐Ÿ”
  • Dynamic SQL ๐ŸŒ€๐Ÿ”ข
  • Materialized Views ๐Ÿ—‚๏ธ๐Ÿ”
  • Database Administration Tasks ๐Ÿ› ๏ธ๐Ÿ“‹

Module 7: Window Functions in SQL ๐ŸชŸ๐Ÿ”ข

  • Introduction to Window Functions ๐Ÿ“š๐ŸชŸ
  • Analytical Functions ๐Ÿ”๐Ÿ“ˆ
  • Aggregating Data Using Window Functions ๐Ÿ“Š๐Ÿ”ข
  • Partitioning Data and Applying Window Functions ๐Ÿ“‹๐Ÿ”„

Module 1: Data Preprocessing with Google Play Store ๐Ÿ“ฑ๐Ÿ”ง

  • Introduction to EDA ๐Ÿ“Š๐Ÿ”
  • Data Cleaning ๐Ÿงน๐Ÿ—ƒ๏ธ
  • Data Visualization ๐Ÿ“ˆ๐ŸŽจ
  • Data Analysis ๐Ÿ”๐Ÿ“‰

Module 2: Advanced Data Preprocessing with Google Play Store ๐Ÿ“ฑ๐Ÿ”

  • Data Preprocessing โ€“ Removing Null Value Rows ๐Ÿšซ๐Ÿ“‹
  • Data Analysis โ€“ Numeric ๐Ÿ”ข๐Ÿ“Š
  • Data Analysis โ€“ Categorical ๐Ÿ—‚๏ธ๐Ÿ”
  • Data Analysis โ€“ Automatic Categorical ๐Ÿค–๐Ÿ—‚๏ธ
  • Null Values Handling โ€“ Numeric ๐Ÿ”ขโ“
  • Null Values Handling โ€“ Categorical ๐Ÿ—‚๏ธโ“
  • Null Values Handling Overall ๐Ÿ—ƒ๏ธโ“

Module 3: Introduction to EDA ๐Ÿ“Š๐Ÿ”

  • Introduction to EDA ๐Ÿ“š๐Ÿ”Ž
  • Understanding Your Data ๐Ÿค”๐Ÿ“ˆ

Module 4: Data Cleaning ๐Ÿงน๐Ÿ”ง

  • Dealing with Missing Values โ“๐Ÿ”
  • Dealing with Duplicate Data ๐Ÿ”„๐Ÿšซ
  • Outliers ๐Ÿšจ๐Ÿ“Š
  • Outlier Removal Using Z-Score ๐Ÿ”ข๐Ÿ“‰
  • Outlier Removal Using IQR ๐Ÿ“๐Ÿ”„
  • Outlier Removal Using Percentile ๐Ÿ“ˆ๐Ÿ”ข
  • Correction of Data Type ๐Ÿ”„๐Ÿ“Š

Module 5: Data Visualization ๐Ÿ“Š๐ŸŽจ

  • Univariate Analysis (Non-Graphical) ๐Ÿ“‹๐Ÿ”
  • Univariate Visualizations (Categorical) ๐Ÿ—‚๏ธ๐Ÿ“ˆ
  • Univariate Visualizations (Numerical) ๐Ÿ”ข๐Ÿ“‰
  • Bivariate Analysis (Numerical-Categorical) ๐Ÿ”ข๐Ÿ—‚๏ธ
  • Bivariate Visualizations (Categorical) ๐Ÿ—‚๏ธ๐Ÿ”
  • Bivariate Visualization (Numerical) ๐Ÿ”ข๐Ÿ”

Module 6: Data Analysis ๐Ÿ”๐Ÿ“Š

  • Data Analysis with Multiple Columns ๐Ÿ“Š๐Ÿ”ข
  • Data Analysis Using Conditions ๐Ÿ”„๐Ÿ”
  • GroupBy in Pandas ๐Ÿ“Š๐Ÿ”—

Introduction to PowerBI ๐Ÿ“Š๐Ÿ”

  • Overview of PowerBI ๐ŸŒ๐Ÿ–ฅ๏ธ
  • PowerBI Interface Tour ๐Ÿ–ฑ๏ธ๐Ÿ“‹
  • PowerBI Desktop vs. PowerBI Service ๐Ÿ’ปโ˜๏ธ
  • Connecting to Data Sources ๐Ÿ”—๐Ÿ“Š
  • Creating Your First Report ๐Ÿ“๐Ÿ“Š

Understanding the Parameters โš™๏ธ๐Ÿ“‹

  • What Are Parameters? โ“๐Ÿ”
  • Creating Parameters โœ๏ธ๐Ÿ”ง
  • Using Parameters in Queries ๐Ÿ”„๐Ÿ“ˆ
  • Parameter Controls and Their Use ๐Ÿ“Š๐ŸŽ›๏ธ
  • Dynamic Parameter Values ๐Ÿ”„๐Ÿ“‰

Fundamentals of PowerBI ๐Ÿ“š๐Ÿ’ก

  • Data Import and Transformation ๐Ÿ”„๐Ÿ“ฅ
  • Data Modeling Basics ๐Ÿ“Š๐Ÿ”—
  • Building and Managing Relationships ๐Ÿ”—๐Ÿ—‚๏ธ
  • Introduction to DAX (Data Analysis Expressions) ๐Ÿ”ข๐Ÿ“
  • Creating Basic Visualizations ๐Ÿ“‰๐Ÿ”ง

ย 

Module 1: Getting Started with Streamlit ๐Ÿš€๐Ÿ–ฅ๏ธ

  • Introduction to Streamlit ๐Ÿ“š๐Ÿ”
  • Streamlit Setup ๐Ÿ› ๏ธ๐Ÿ“ฅ
  • Basic Output Tags ๐Ÿท๏ธ๐Ÿ“
  • Inspecting the Website ๐Ÿ”๐ŸŒ
  • Text Input ๐Ÿ“๐Ÿ” 
  • Special Input with Buttons ๐Ÿ”˜โœจ
  • Forms ๐Ÿ“๐Ÿ“‹
  • Integrating Scripts ๐Ÿ”—๐Ÿ“œ

Module 2: Page Beautification ๐ŸŽจ๐Ÿ–ผ๏ธ

  • Working with Columns ๐Ÿ“Š๐Ÿ—‚๏ธ
  • Working with Tabs ๐Ÿ“‘๐Ÿ”„
  • Expander & Empty Functionalities ๐Ÿ”ฝ๐Ÿ“‚
  • Advanced Display & Progress Options ๐Ÿ“ˆ๐Ÿ”ง
  • Echo and Stop Commands ๐Ÿ”„โน๏ธ

Module 3: Working with Data ๐Ÿ“Š๐Ÿ”—

  • Working with Media Files ๐ŸŽฅ๐Ÿ“
  • DataFrames with Streamlit ๐Ÿ—ƒ๏ธ๐Ÿ“Š
  • File Uploading โฌ†๏ธ๐Ÿ“‚
  • Image Converter ๐Ÿ–ผ๏ธ๐Ÿ”„
  • Image Rotation ๐Ÿ”„๐Ÿ–ผ๏ธ

Module 4: Introduction to Data Visualization ๐Ÿ“ˆ๐Ÿ”

  • Getting Started with Basic Plots with Streamlit ๐Ÿ“Š๐Ÿ”ง
  • Plots with Matplotlib and Seaborn ๐Ÿ“‰๐ŸŽจ
  • Visualization with Plotly ๐Ÿ“Š๐Ÿ”

Module 5: Deployment ๐Ÿš€๐ŸŒ

  • Deployment on Streamlit Server ๐ŸŒ๐Ÿ–ฅ๏ธ

Introduction to Statistics ๐Ÿ“Š๐Ÿ”

  • What is Statistics? โ“๐Ÿ“š
  • Importance of Statistics ๐Ÿ“ˆ๐Ÿ”
  • Applications of Statistics ๐Ÿฅ๐Ÿ“‰
  • Statistical Methods Overview ๐Ÿ“‹๐Ÿ› ๏ธ

Data and Their Types ๐Ÿ“Š๐Ÿ”ข

  • Types of Data ๐Ÿ—ƒ๏ธ๐Ÿ” 
    • Quantitative Data ๐Ÿ”ข๐Ÿ“
    • Qualitative Data ๐Ÿ—‚๏ธ๐Ÿ”ก
  • Levels of Measurement ๐ŸŽš๏ธ๐Ÿ”ข
    • Nominal ๐Ÿท๏ธ
    • Ordinal ๐Ÿ“ˆ
    • Interval ๐Ÿ“…
    • Ratio ๐Ÿ”ข๐Ÿ“

Introduction to Descriptive Stats ๐Ÿ“Š๐Ÿ“‹

  • What is Descriptive Statistics? ๐Ÿ“ˆ๐Ÿ”
  • Purpose of Descriptive Stats ๐ŸŽฏ๐Ÿ”
  • Types of Descriptive Statistics ๐Ÿ“Š๐Ÿ”ข

Measures of Frequency ๐Ÿ“Š๐Ÿ”ข

  • Frequency Distribution ๐Ÿ“‰๐Ÿ“‹
  • Frequency Tables ๐Ÿ“Š๐Ÿ—‚๏ธ
  • Histograms ๐Ÿ“ˆ๐Ÿ”ข
  • Frequency Polygons ๐Ÿ“‰๐Ÿ”—

Measures of Central Tendency ๐Ÿงฎ๐Ÿ“ˆ

  • Mean โž—๐Ÿ”ข
  • Median ๐Ÿ“๐Ÿ”ข
  • Mode ๐Ÿ”๐Ÿ”ข
  • Comparing Measures of Central Tendency ๐Ÿ”„๐Ÿ“ˆ

Measures of Dispersion ๐Ÿ“๐Ÿ“Š

  • Range ๐Ÿ†๐Ÿ”ข
  • Variance ๐Ÿ“ˆ๐Ÿ”ข
  • Standard Deviation ๐Ÿ“Š๐Ÿ”ข
  • Interquartile Range (IQR) ๐Ÿ“‰๐Ÿ”ข

Measures of Shape ๐Ÿ“๐Ÿ“Š

  • Skewness ๐Ÿž๏ธ๐Ÿ”ข
  • Kurtosis ๐Ÿ”๏ธ๐Ÿ”ข
  • Symmetry of Distribution ๐Ÿ”„๐Ÿ“ˆ
  • Machine Learning Introduction ๐Ÿค–๐Ÿ“š
  • Machine Learning Advanced ๐Ÿš€๐Ÿ“Š
  • Introduction to AI and Deep Learning ๐Ÿค–๐Ÿ”
  • Natural Language Processing ๐Ÿ—ฃ๏ธ๐Ÿ”
  • Generative AI ๐Ÿง ๐ŸŽจ
  • ChatGPT ๐Ÿ—จ๏ธ๐Ÿค–
  • What is AI.
  • What is Machine Learning?.
  • Understanding Data and Terminology.
  • Types of Learning
    Regression vs Classification.
  • Subset Of AI
  • Introduction โ€“ Neurons vs Artificial Neural Networks.
  • Learning of ANN.
  • How to Implement and Visualize a Perceptron in Artificial Neural Networks?
  • Getting Started with NLTK and Tokenization.
  • Stemming & Lemmatisation.
  • StopWords Removal from Scratch.
  • Corpus & Vocabulary
    Vocabulary with Keras

3 Months

90+ Learning Hours

Tool Box

5+ Assured Interviews

Who Should Take This Course?

Graduates and Postgraduates
Fresherโ€™s
Working Professionals
No Prior Knowledge is required

What Are The Roles Can You Apply

See Yourself In One Of These Roles?

THE TEACHER

SHIVA

I love teaching Data Science because it allows me to share the exciting world of data analysis and machine learning with students. I strive to make each lesson clear and practical, so students can apply what they learn right away. Itโ€™s amazing to watch them grow from beginners to skilled data scientists! I refer this training center both Online & Offline. I can handle both platforms for my students convenience.

SUCESS STORIES

K. Naveen

I have done this course Online. Glass Konnect team is very good people and friendly staff. I didnโ€™t know about Data Science clearly what it is. Glass Konnect team helped me to complete my entire course. I have sucussesfully done it. As a complete beginner, I was a bit intimidated at first, but this Data Science Course was perfect for me. It started with the basics and gradually built up to more advanced topics like deep learning and neural networks. The instructors were fantastic, offering great support throughout. I am excited to apply my new skills in the real world and begin my career in data science!

Y. Anitha

The Data Science Training exceeded my expectations. The content was comprehensive, covering everything from data cleaning and visualization to machine learning and statistical modeling. I loved the practical assignments that gave me a chance to work with real datasets. I now feel well-prepared to tackle data science projects in my job, and the knowledge I gained has opened up new career opportunities. I have completed my course offline. I strongly recommend this institute for Data Science Training for Beginners.

G. KUMAR

I took the Data Science course to enhance my skills for career advancement, and it delivered on every level. The curriculum is well-structured, and the hands-on exercises made complex concepts easier to grasp. I particularly appreciated the focus on Python and machine learning. This course gave me the tools I need to make data-driven decisions and provided me with the confidence to pursue a data science career.

M. SHANTHI

This course was a game-changer for me. Iโ€™ve always been interested in data but didnโ€™t know how to make the transition into data science. The instructors were very knowledgeable, and the course covered a wide range of topics, including data wrangling, analysis, and visualization. The practical projects and real-life applications made it all come to life, and I feel ready to take on data science challenges in the professional world.

Pricing

Affordable Pricing Packages

Online Batchโ€‹

Basic Package

online batch is a virtual training session where you can join and learn in real-time from anywhere

What's included?

*Terms and Conditions apply

Offline Batchโ€‹

Regular Package

offline batch at Glass Konnect offers in-person software training at our physical location in Hyderabad.

What's included?

*Terms and Conditions apply

Need a custom pricing plan?

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