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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 industries—such as healthcare, finance, marketing, and technology—to make informed decisions, optimize processes, and innovate solutions.
- Certified
- 90 Days
- 50 Students
- 173 Batches
THE COURSE INCLUDE
- Real Time Projects
- Job Assessment
- Mock Interviews
CURRICULUM
- Overview of Data Science 🌐
- Key Roles & Responsibilities 🧑💼
- Essential Tools & Technologies 🛠️.
- Career Path in Data Science Fields 🤑
- Roles in Data Science Field.
Module 1: Exploring Data 🔍📊
- Welcome to Excel: An Overview of the World’s Most Popular Spreadsheet Software 🖥️📈
- Excel Beyond Limits 🚀📊
- Mastering the Basics of Spreadsheet Navigation 🧭📋
- Working on Rows and Columns 📏🔢
- Looking for Exact Matches 🔍✔️
- Trimming Function and Its Usage ✂️📉
- Sorting the Data 🔢🔄
- Nesting Functions 🏗️🔧
- Data Types in Excel 📊🔠
- Type Conversion in Excel 🔄🔢
Module 2: Preparing Data 🛠️📋
- Mastering Text Functions in Excel: CONCATENATE, UPPER, LOWER, and PROPER ✍️🔤
- Mastering Text Extraction in Excel: LEFT, RIGHT, and SUBSTITUTE Functions ✂️🔍
- Mastering Excel’s DATE Function: Effective Techniques for Date Handling 📅🛠️
- Excel DATEDIF Function (Between Two Dates) 📅↔️📅
- How To Use Relative & Absolute Cell References in Excel 🔄🔒
- Harnessing the Power of VLOOKUP for In-depth Insights 🔍🔎
- Unleashing the Power of SUMIF Function in Excel 🔍➕
Module 3: Analyzing the Data 📈🔍
- Mastering the COUNT, COUNTA, and COUNTBLANK Functions 🧮🔢📊
- Unleashing the Power of COUNTIF Function in Excel 📊🔍
- Using Excel’s Core Calculation Functions: A Practical Guide 📈🛠️
- Excel Logic Functions Explained: IF, AND, and OR in Practice 🤔🔄
- Organizing Data with UNIQUE and SORT Functions 🗂️🔄
Module 1: SQL Fundamentals 📚💻
- SQL Installation & Setup 🛠️🔧
- Types of SQL Commands 📜🗂️
- DDL Commands for Database and Tables 🏗️📋
- SQL Constraints 🚫📊
Module 2: Data Manipulation with SQL (DML) ✏️🔄
- Modifying Data in Tables ✍️📋
- Retrieving Data with SQL 🔍📊
Module 3: Intermediate SQL Queries 🔍📈
- Selecting Columns 📋🔢
- Filtering Rows 🔍🗂️
- Aggregate Functions 📊➕
- Sorting & Grouping 🔢📊
- Null Values ❓📉
- Date and Time Functions 📅⏰
- Working with Expressions 🧮🔢
- Order of Execution of SQL Commands 🔄🗂️
Module 4: Joining & Combining Data 🔗🔄
- Types of Joins 🔍🔗
- Left & Right Join ⬅️➡️
- Inner Join and Full Join 🔄🔁
- Cross Join and Self Join 🔄👤
- Understanding Table Relationships 🗂️🔗
Module 5: Data Preprocessing & Analysis 🔧📊
- Handling Missing Values ❓📉
- Handling Duplicates 🔄🚫
- Data Transformation 🔄🛠️
- Working with Dates and Times 📅⏳
- Data Filtering and Selection 🔍📋
- Analyzing Time Series Data 📈⏳
- Performance Optimization ⚡🔧
- Working with JSON and XML Data 📄🔢
- Data Blending 🥤🔄
Module 6: Advanced SQL 🚀📊
- SQL Views 👁️📋
- Triggers 🚨🔄
- Performance Tuning ⚙️🚀
- Backup and Recovery 💾🔄
- Advanced Joins 🔗🔍
- Dynamic SQL 🌀🔢
- Materialized Views 🗂️🔍
- Database Administration Tasks 🛠️📋
Module 7: Window Functions in SQL 🪟🔢
- Introduction to Window Functions 📚🪟
- Analytical Functions 🔍📈
- Aggregating Data Using Window Functions 📊🔢
- Partitioning Data and Applying Window Functions 📋🔄
Module 1: Data Preprocessing with Google Play Store 📱🔧
- Introduction to EDA 📊🔍
- Data Cleaning 🧹🗃️
- Data Visualization 📈🎨
- Data Analysis 🔍📉
Module 2: Advanced Data Preprocessing with Google Play Store 📱🔍
- Data Preprocessing – Removing Null Value Rows 🚫📋
- Data Analysis – Numeric 🔢📊
- Data Analysis – Categorical 🗂️🔍
- Data Analysis – Automatic Categorical 🤖🗂️
- Null Values Handling – Numeric 🔢❓
- Null Values Handling – Categorical 🗂️❓
- Null Values Handling Overall 🗃️❓
Module 3: Introduction to EDA 📊🔍
- Introduction to EDA 📚🔎
- Understanding Your Data 🤔📈
Module 4: Data Cleaning 🧹🔧
- Dealing with Missing Values ❓🔍
- Dealing with Duplicate Data 🔄🚫
- Outliers 🚨📊
- Outlier Removal Using Z-Score 🔢📉
- Outlier Removal Using IQR 📏🔄
- Outlier Removal Using Percentile 📈🔢
- Correction of Data Type 🔄📊
Module 5: Data Visualization 📊🎨
- Univariate Analysis (Non-Graphical) 📋🔍
- Univariate Visualizations (Categorical) 🗂️📈
- Univariate Visualizations (Numerical) 🔢📉
- Bivariate Analysis (Numerical-Categorical) 🔢🗂️
- Bivariate Visualizations (Categorical) 🗂️🔍
- Bivariate Visualization (Numerical) 🔢🔍
Module 6: Data Analysis 🔍📊
- Data Analysis with Multiple Columns 📊🔢
- Data Analysis Using Conditions 🔄🔍
- GroupBy in Pandas 📊🔗
Introduction to PowerBI 📊🔍
- Overview of PowerBI 🌐🖥️
- PowerBI Interface Tour 🖱️📋
- PowerBI Desktop vs. PowerBI Service 💻☁️
- Connecting to Data Sources 🔗📊
- Creating Your First Report 📝📊
Understanding the Parameters ⚙️📋
- What Are Parameters? ❓🔍
- Creating Parameters ✍️🔧
- Using Parameters in Queries 🔄📈
- Parameter Controls and Their Use 📊🎛️
- Dynamic Parameter Values 🔄📉
Fundamentals of PowerBI 📚💡
- Data Import and Transformation 🔄📥
- Data Modeling Basics 📊🔗
- Building and Managing Relationships 🔗🗂️
- Introduction to DAX (Data Analysis Expressions) 🔢📏
- Creating Basic Visualizations 📉🔧
Module 1: Getting Started with Streamlit 🚀🖥️
- Introduction to Streamlit 📚🔍
- Streamlit Setup 🛠️📥
- Basic Output Tags 🏷️📝
- Inspecting the Website 🔍🌐
- Text Input 📝🔠
- Special Input with Buttons 🔘✨
- Forms 📝📋
- Integrating Scripts 🔗📜
Module 2: Page Beautification 🎨🖼️
- Working with Columns 📊🗂️
- Working with Tabs 📑🔄
- Expander & Empty Functionalities 🔽📂
- Advanced Display & Progress Options 📈🔧
- Echo and Stop Commands 🔄⏹️
Module 3: Working with Data 📊🔗
- Working with Media Files 🎥📁
- DataFrames with Streamlit 🗃️📊
- File Uploading ⬆️📂
- Image Converter 🖼️🔄
- Image Rotation 🔄🖼️
Module 4: Introduction to Data Visualization 📈🔍
- Getting Started with Basic Plots with Streamlit 📊🔧
- Plots with Matplotlib and Seaborn 📉🎨
- Visualization with Plotly 📊🔍
Module 5: Deployment 🚀🌐
- Deployment on Streamlit Server 🌐🖥️
Introduction to Statistics 📊🔍
- What is Statistics? ❓📚
- Importance of Statistics 📈🔍
- Applications of Statistics 🏥📉
- Statistical Methods Overview 📋🛠️
Data and Their Types 📊🔢
- Types of Data 🗃️🔠
- Quantitative Data 🔢📏
- Qualitative Data 🗂️🔡
- Levels of Measurement 🎚️🔢
- Nominal 🏷️
- Ordinal 📈
- Interval 📅
- Ratio 🔢📏
Introduction to Descriptive Stats 📊📋
- What is Descriptive Statistics? 📈🔍
- Purpose of Descriptive Stats 🎯🔍
- Types of Descriptive Statistics 📊🔢
Measures of Frequency 📊🔢
- Frequency Distribution 📉📋
- Frequency Tables 📊🗂️
- Histograms 📈🔢
- Frequency Polygons 📉🔗
Measures of Central Tendency 🧮📈
- Mean ➗🔢
- Median 📍🔢
- Mode 🔁🔢
- Comparing Measures of Central Tendency 🔄📈
Measures of Dispersion 📏📊
- Range 🏆🔢
- Variance 📈🔢
- Standard Deviation 📊🔢
- Interquartile Range (IQR) 📉🔢
Measures of Shape 📏📊
- Skewness 🏞️🔢
- Kurtosis 🏔️🔢
- Symmetry of Distribution 🔄📈
- Machine Learning Introduction 🤖📚
- Machine Learning Advanced 🚀📊
- Introduction to AI and Deep Learning 🤖🔍
- Natural Language Processing 🗣️🔍
- Generative AI 🧠🎨
- ChatGPT 🗨️🤖
- What is AI.
- What is Machine Learning?.
- Understanding Data and Terminology.
- Types of Learning
Regression vs Classification. - Subset Of AI
- Introduction – 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
OTHER COURSES
Who Should Take This Course?
What Are The Roles Can You Apply
See Yourself In One Of These Roles?
Tools & Technologies
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. It’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.
SUCCESS STORIES
K. Naveen
I have done this course Online. Glass Konnect team is very good people and friendly staff. I didn’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!
- 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.
- Data Science
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.
- Data Science
M. SHANTHI
This course was a game-changer for me. I’ve always been interested in data but didn’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.
- Data Science
Online Batch
Basic Package
online batch is a virtual training session where you can join and learn in real-time from anywhere
What's included?
- Fee details
- Program Fee ₹ 70,000
- 24/7 Full Support
*Terms and Conditions apply
Offline Batch
Regular Package
offline batch at Glass Konnect offers in-person software training at our physical location in Hyderabad.
What's included?
- Fee details
- Fees ₹ 80,000
- 24/7 Full Support
*Terms and Conditions apply
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