Data Analytics, Data Science, & Machine Learning - All In 1

From Theory to Hands-on Projects - EVERYTHING to Master Data Analytics, Data Science and Machine Learning in 1 Course.

What you'll learn

Understand data science foundations, applications, and the path to becoming a data scientist.

Analyze data using Python programming with variables, loops, functions, and OOP.

Apply statistics and probability with distributions, hypothesis testing, and inference in Python.

Perform data cleaning, transformation, and EDA using pandas and NumPy.

Visualize data with Python using bar charts, histograms, scatterplots, heatmaps, and box plots.

Build regression, classification, and clustering models with scikit-learn and evaluate performance.

Master advanced ML techniques like cross-validation, feature engineering, regularization, and hyperparameter tuning.

Implement ensemble learning methods such as Random Forest, AdaBoost, CatBoost, LightGBM, and XGBoost.

Explore deep learning with neural networks and TensorFlow, from preprocessing to model evaluation.

Gain hands-on experience through real-life projects and assessments to build a strong portfolio.

Acquire Excel, SQL, Python, Power BI, and ChatGPT skills to prepare for a data analyst career.

Learn data analysis foundations with statistics, hypothesis testing, and machine learning.

Use Excel for data cleaning, manipulation, formulas, functions, graphs, and charts.

Apply Excel advanced tools like pivot tables, Analysis ToolPak, and interactive dashboards.

Understand RDBMS fundamentals including keys, data types, and relational models.

Work with MySQL for table manipulation, constraints, indices, filtering, and joins.

Learn Python basics including variables, data types, lists, dictionaries, loops, and functions.

Master Python for data cleaning, manipulation, preprocessing, and transformation.

Use Python for visualization, exploratory analysis, statistics, and ML modeling.

Utilize ChatGPT for data manipulation, merging, pivot tables, and conditional logic.

Apply ChatGPT for predictive analytics with Random Forest and ML models.

Learn Power BI for data manipulation, analysis, and dashboard insights.

Create professional, story-driven dashboards in Power BI with impactful visuals.

Complete 30+ assignments, 120+ coding exercises, and 10 quizzes with 100+ questions.

Accomplish 4 capstone projects: bank churn analysis, sports analytics, HR data management and website performance analysis.

Accomplish 7 AI projects: Image Captioning, Chatbot, Voice Assistant, Text to Image, Video Summarizer, Language Translator and Data Analyst AI

Requirements

Access to computer and internet

Basic computer literacy

No coding experience required

Dedication, patience and perseverance

Description

Embark on a transformative journey into the world of Data Analytics, Data Science, and Machine Learning, where you’ll learn the essential skills, tools, and mindsets to become a successful data professional. This comprehensive program is designed to take you from beginner to advanced, equipping you with the knowledge and practical experience needed to excel in the field.Whether you’re looking to kickstart a career in data analytics or enhance your existing skills, this course will empower you to succeed in the dynamic world of data. Join us on this exciting path and unlock your potential in just 60–100 days of disciplined learning.Why This Course MattersMost learners struggle with fragmented resources, inconsistent guidance, or theory-heavy content that doesn’t build real competence. This course solves that problem. It’s structured to provide step-by-step, cumulative, and daily progress — helping you turn knowledge into capability, and capability into career readiness.We are in the AI revolution, and every industry is transforming with tools like ChatGPT, Stable Diffusion, and AI copilots for writing, coding, design, analytics, and more. This course ensures you don’t just learn theory — you’ll build real-world solutions that make you job-ready.1. Foundations of Data Analytics, Data Science & PythonLearn how to think like a data scientist, not just how to write code.Python fundamentals: variables, loops, conditionals, functions, data structures.Clean, modular, reusable coding practices for data workflows.Importing and handling real-world datasets with Pandas and NumPy.Data types, memory optimization, and performance tuning.A-Z data cleaning and manipulation techniques: sorting, filtering, pivot tables, and charts.2. Excel, SQL, Python & Power BI ProficiencyExcel: Manipulate data, perform calculations, and create visualizations.SQL: Query and manipulate relational databases, perform joins, aggregations, and optimize queries.Python: Analyze and visualize data with Pandas, NumPy, and Matplotlib. Automate workflows and create advanced dashboards.ChatGPT for Data Analysis: Handle missing data, outliers, dataset merging, pivoting, and even advanced ML predictions.Power BI: Connect to multiple data sources, clean and transform data, and design interactive dashboards and reports.3. Exploratory Data Analysis (EDA)Understand the shape, distributions, and essence of raw data.Advanced grouping, filtering, and reshaping with Pandas.Visualize relationships using Matplotlib and Seaborn (histograms, pairplots, heatmaps).Develop strong data intuition and hypothesis-forming skills.4. Probability, Statistics & Mathematics for Data ScienceProbability distributions: Normal, Binomial, Poisson, Exponential, Uniform.Descriptive statistics: mean, median, mode, variance, standard deviation.Inferential statistics: confidence intervals, hypothesis testing, chi-square, t-tests, ANOVA.Linear Algebra: vectors, matrices, dot products, PCA foundations.Calculus: derivatives, gradients, optimization, and gradient descent for ML.5. Machine Learning & Feature EngineeringComplete ML workflow: preprocessing, training, validating, testing.Algorithms: Logistic Regression, Decision Trees, Random Forests, KNN, Ensemble Methods.Handling class imbalance (SMOTE, stratified sampling).Model evaluation: accuracy, precision, recall, F1-score, ROC-AUC.Bias-variance tradeoff, underfitting vs. overfitting.Feature engineering: encoding categorical variables, scaling/normalizing, building pipelines.Hyperparameter tuning (GridSearchCV, RandomizedSearchCV).6. Deep Learning & Generative AINeural networks with TensorFlow: tensors, activation functions, backpropagation, optimizers.Build and train models step by step, fine-tune, and evaluate with accuracy/loss metrics.Prompt Engineering: Chain-of-Thought, Tree-of-Thought, structured prompts.Generative AI Tools & Use Cases: text, image, code, audio, and video generation.Real-world AI applications: chatbots, translators, voice assistants, text-to-image, video summarization.7. Projects & Hands-On PracticeOver 30+ assignments, 120+ coding exercises, and 10 quizzes.Capstone Projects:Bank Data AnalysisSports Data AnalysisFraud Detection & ClassificationStriker Ranking (End-to-End ML Deployment)Generative AI Projects (7 full-scale builds):Image Captioning AIChatbot with LLaMA2/GemmaAI Voice AssistantText-to-Image GeneratorAI Video SummarizerLanguage TranslatorAI Data AnalystBenefits of the CourseCareer Readiness: Gain the technical and professional skills to qualify for data analyst and data scientist roles.Versatility: Become proficient in Excel, SQL, Python, Power BI, TensorFlow, Hugging Face, and more.Problem-Solving Skills: Sharpen your analytical and critical thinking abilities.Portfolio Enhancement: Build a robust portfolio of real-world projects to showcase in interviews.Industry-Relevant Learning: Stay up-to-date with modern data and AI methodologies.How This Course Will Transform YouBy following this structured roadmap, you’ll be able to:Confidently work with real datasets and perform independent analysis.Build, tune, and deploy machine learning and AI models.Understand the mathematical foundations of modern data science.Create a project portfolio strong enough for job interviews or freelance opportunities.Qualify for entry-to-intermediate level roles in Data Science, ML Engineering, or Analytics.One Honest LimitationThis course is not for learners who prefer highly animated, passive learning. The teaching style is text-based, code-first, and explanation-rich — emphasizing depth, clarity, and practical application. Diagrams and visuals are included, but the focus is on doing, thinking, and building.

Overview

Section 1: Warm Up + Important Message

Lecture 1 How to take the classes

Lecture 2 Take a refund now, if….

Section 2: Data Science

Lecture 3 Important Messages for You!

Lecture 4 What is Data Science

Lecture 5 Fundamentals of Data Science

Lecture 6 The path to be a Data Scientist

Lecture 7 Data Analysis

Lecture 8 Business Intelligence

Lecture 9 Statistical Modeling

Lecture 10 Machine Learning

Lecture 11 Deep Learning

Lecture 12 Artificial Intelligence

Lecture 13 Traditional Data vs Big Data

Lecture 14 Working with Big Data

Lecture 15 Real - life examples of Big data

Lecture 16 Database management tools

Lecture 17 Programming languages

Lecture 18 360 Data analytics tools

Lecture 19 Data visualization tools

Lecture 20 Development environments

Lecture 21 Step 1 - Business understanding

Lecture 22 Step 2 - Data collection

Lecture 23 Step 3 - Data preparation

Lecture 24 Step 4 - Data modeling

Lecture 25 Step 5 - Model evaluation

Lecture 26 Step 6 - Model deployment

Section 3: Data Analytics

Lecture 27 Extra note on analytical world of data

Lecture 28 Data analysis definition, types and examples

Lecture 29 Tools and technologies for data analysis

Lecture 30 Various sources of collecting data

Lecture 31 Population v/s sample and its methods

Lecture 32 The first requirement - Data cleaning!

Lecture 33 Various methods of data cleaning

Lecture 34 Various aspects of Joining datasets

Lecture 35 Adding extra data with concatenation

Lecture 36 EDA for generating significant insights

Lecture 37 Methods of exploratory data analysis Part 1

Lecture 38 Methods of exploratory data analysis Part 2

Lecture 39 Methods of exploratory data analysis Part 3

Lecture 40 The application of statistical test

Lecture 41 Types of statistical data analysis

Lecture 42 T-tests and ANOVA

Lecture 43 Relationships measures

Lecture 44 Regression analysis

Lecture 45 Probability in data analysis

Lecture 46 Classical probability

Lecture 47 Empirical probability

Lecture 48 Conditional probability

Lecture 49 Joint probability

Lecture 50 Hypothesis testing for inferential statistics

Lecture 51 Selecting statistical test and assumption testing

Lecture 52 Confidence level, significance level, p-value

Lecture 53 Making decision and conclusion on findings

Lecture 54 Complete statistical analysis and hypothesis testing

Lecture 55 Transforming data for improved analysis

Lecture 56 Techniques for data transformation Part 1

Lecture 57 Techniques for data transformation Part 2

Lecture 58 Several methods of data visualization Part 1

Lecture 59 Several methods of data visualization Part 2

Lecture 60 Several methods of data visualization Part 3

Section 4: Machine Learning, Deep Learning & AI

Lecture 61 ML for data analysis and decision-making

Lecture 62 Widely used ML methods in the data analytics

Lecture 63 Steps in developing machine learning model

Lecture 64 What is Machine learning?

Lecture 65 Supervised Regression models

Lecture 66 Supervised Classification models

Lecture 67 Unsupervised clustering models

Lecture 68 Model evaluating metrics

Lecture 69 Overfitting & Underfitting

Lecture 70 Imbalanced data problem

Lecture 71 What is Matrix?

Lecture 72 Scalars and Vectors

Lecture 73 Linear algebra introduction

Lecture 74 What is Tensor?

Lecture 75 Transpose of Matrix

Lecture 76 Dot product and Matrix

Lecture 77 How Linear regression works

Lecture 78 How Logistic regression works

Lecture 79 K-fold cross validation

Lecture 80 L1, L2 regularization

Lecture 81 The oversampling method

Lecture 82 The undersampling method

Lecture 83 How KMeans clustering works

Lecture 84 How Decision tree regression works

Lecture 85 How Decision tree classification works

Lecture 86 How Random forest regression works

Lecture 87 How Random forest classification works

Lecture 88 How AdaBoost Models work

Lecture 89 How Traditional GBM works

Lecture 90 How CatBoost Models work

Lecture 91 How LightGBM Models work

Lecture 92 How XGBoost Models work

Lecture 93 What is Hyperparameter tuning?

Lecture 94 Understanding Deep Learning

Lecture 95 Neural Networks in Deep Learning

Lecture 96 What is TensorFlow?

Lecture 97 How TensorFlow 2.0 works

Lecture 98 What is Initialization?

Lecture 99 Glorot Initialization

Lecture 100 Stochastic Gradient Descent

Lecture 101 AI history, definition and workflow

Lecture 102 Various types of Artificial intelligence

Lecture 103 Artificial v/s Augmented Intelligence

Lecture 104 Generative AI and Its use cases

Lecture 105 Traditional AI v/s Generative AI

Lecture 106 Reading material: Types of AI

Lecture 107 AI use cases in Daily life

Lecture 108 What is AI Chatbot?

Lecture 109 Gen AI Tools and Applications

Lecture 110 Reading material: AI and Generative AI

Lecture 111 Various models of Generative AI

Lecture 112 NLP, Speech Technology & Computer vision

Lecture 113 AI, Cloud and Edge computing & IoT

Lecture 114 Reading material: The parts of AI + Gen AI

Lecture 115 Tools for Text Generation

Lecture 116 Tools for Image Generation

Lecture 117 Tools for Code Generation

Lecture 118 Tools for Audio and Video Generation

Lecture 119 Reading material: Gen AI Tools

Lecture 120 What is a Prompt?

Lecture 121 What is Prompt Engineering?

Lecture 122 Best practices in Prompt engineering

Lecture 123 Reading material: prompt engineering tools

Lecture 124 Interview pattern prompt technique

Lecture 125 Chain-of-Thought prompt technique

Lecture 126 Tree-of-Thought prompt technique

Lecture 127 Reading material: Prompt engineering

Section 5: Python Programming Fundamentals

Lecture 128 Installing Python & Jupyter notebook

Lecture 129 Note on python data analysis

Lecture 130 Datasets used in the course

Lecture 131 Understanding Expressions and Variables

Lecture 132 Hands-on Lab: Expressions and Variables

Lecture 133 Understanding Data Types

Lecture 134 Hands-on Lab: Python Data Types

Lecture 135 Various String Operators

Lecture 136 Hands-on Lab: Various String Operators

Lecture 137 Starting with Variables to Data Types

Lecture 138 Understanding Tuples and Lists

Lecture 139 Hands-on: Tuples and Lists

Lecture 140 Operations & Manipulation of Sets

Lecture 141 Hands-on Lab: Sets

Lecture 142 Working with Dictionaries

Lecture 143 Hands-on Lab: Dictionaries

Lecture 144 Several data structures

Lecture 145 Condition and Branching

Lecture 146 Hands-on Lab: Condition & Branching

Lecture 147 Loops for Iteration

Lecture 148 Hands-on Lab: Loops

Lecture 149 Developing Functions

Lecture 150 Hands-on Lab: Python Functions

Lecture 151 Object and Classes

Lecture 152 Hands-on Lab: Object and Classes

Lecture 153 Conditionals Looping and Functions

Lecture 154 API, REST API & Request

Lecture 155 HTML and BeautifulSoup

Lecture 156 Hands-on Lab: BeautifulSoup

Lecture 157 Open() to import data

Lecture 158 Hands-on Lab: Open()

Lecture 159 Reading and Writing with Pandas

Lecture 160 Hands-on Lab: Importing datasets

Lecture 161 Reading and Writing JASON & XML

Lecture 162 Hands-on Lab: Importing JASON & XML

Lecture 163 Exception Handling

Lecture 164 Hands-on Lab: Exception Handling

Lecture 165 Reading material: Errors in Python

Section 6: Deep Dive - Probability and Distribution

Lecture 166 What is probability?

Lecture 167 Expected value v/s Actual value

Lecture 168 Frequency in probability

Lecture 169 Complements in probability

Lecture 170 Intro to combinatorics

Lecture 171 Permutations

Lecture 172 Factorials operations

Lecture 173 Combinations

Lecture 174 Mutually exclusive sets

Lecture 175 Set dependencies

Lecture 176 Conditional probability

Lecture 177 The additive rule

Lecture 178 The multiplication law

Lecture 179 The bayes' law

Lecture 180 Population and Sample

Lecture 181 Types of Statistical data

Lecture 182 Level of Measurement

Lecture 183 Intro to Distributions

Lecture 184 Discrete distributions

Lecture 185 Continuous distributions

Lecture 186 Uniform distribution

Lecture 187 Bernoulli distribution

Lecture 188 Binomial distribution

Lecture 189 Poisson distribution

Lecture 190 Normal distribution

Lecture 191 Students' T distribution

Lecture 192 Chi-squared distribution

Lecture 193 Exponential distribution

Section 7: Deep Dive - EDA Techniques & Statistics

Lecture 194 Understanding Missing values

Lecture 195 Understanding Imputation

Lecture 196 Dataframe's data types

Lecture 197 What is Inconsistent value?

Lecture 198 Understanding duplicates

Lecture 199 What is data sorting?

Lecture 200 Understanding slicing

Lecture 201 Methods of Data filtering

Lecture 202 Understanding data merge

Lecture 203 Understanding concatenation

Lecture 204 Frequency & Percentage

Lecture 205 Mean, Median and Mode

Lecture 206 Skewness and Kurtosis

Lecture 207 Variance and Covariance

Lecture 208 Standard deviation

Lecture 209 Group-by data analysis

Lecture 210 PIVOT table analysis

Lecture 211 Cross-tab analysis

Lecture 212 Use cases of Bar chart

Lecture 213 Use cases of pie chart

Lecture 214 Use cases of line chart

Lecture 215 Use case of Histogram

Lecture 216 Use cases of Scatterplot

Lecture 217 Use cases of Heatmap

Lecture 218 Use cases of Box-plot

Lecture 219 What is inferential statistics

Lecture 220 Central limit theorem

Lecture 221 Standard error

Lecture 222 Estimators and estimates

Lecture 223 Confidence interval

Lecture 224 Z-score v/s T-score

Lecture 225 Margin of error

Lecture 226 Null v/s Alternative Hypothesis

Lecture 227 Type | and Type || Error

Lecture 228 Step 1: Formulate the Hypotheses

Lecture 229 Step 2: Select Significance level

Lecture 230 Step 3: Perform assumption test

Lecture 231 Step 4: Perform appropriate test

Lecture 232 Step 5: Decision and Conclusion

Lecture 233 Kdeplot for distribution

Lecture 234 Shapiro Wilk test

Lecture 235 Data transformations methods

Lecture 236 Independent sample t-test

Lecture 237 Analysis of Variance

Lecture 238 Chi square test

Lecture 239 Pearson correlation

Lecture 240 Linear regression analysis

Lecture 241 How to generate new feature?

Lecture 242 Extracting date elements

Lecture 243 When to encode feature

Lecture 244 When to bin feature

Lecture 245 When to map feature

Lecture 246 When to generate dummies

Lecture 247 Feature selection

Lecture 248 Methods of Feature scaling

Lecture 249 What is Dimensionality reduction?

Lecture 250 Splitting Dataset

Section 8: Python - Complete Data Science, Machine & Deep Learning

Lecture 251 Datasets for this section

Lecture 252 Importing dataset

Lecture 253 Python for missing value identification

Lecture 254 Hands-on: Missing value identification

Lecture 255 Python for missing value imputation

Lecture 256 Hands-on: Imputing missing values

Lecture 257 Python for casting data types

Lecture 258 Hands-on: Data types in dataframe

Lecture 259 Python for dealing inconsistencies

Lecture 260 Hands-on: Working with inconsistencies

Lecture 261 Python for dealing duplicates

Lecture 262 Hands-on Working with duplicates

Lecture 263 Python for sorting data

Lecture 264 Hands-on: Sorting data

Lecture 265 Python for data slicing

Lecture 266 Hands-on: Data slicing

Lecture 267 Python for data filtering

Lecture 268 Hands-on: Data filtering

Lecture 269 Python for merging data

Lecture 270 Hands-on: Merging dataframes

Lecture 271 Python for concatenation

Lecture 272 Hands-on: Data concatenation

Lecture 273 Python for frequency-percentage analysis

Lecture 274 Hands-on: Frequency & Percentage

Lecture 275 Python for descriptive analysis

Lecture 276 Hands-on: Descriptive analysis

Lecture 277 Python for group-by analysis

Lecture 278 Hands-on: Group-by analysis

Lecture 279 Python for PIVOT table analysis

Lecture 280 Hands-on: PIVOT table analysis

Lecture 281 Python for Cross-tab analysis

Lecture 282 Hands-on: Cross-tab analysis

Lecture 283 Python for creating bar chart

Lecture 284 Hands-on: Creating bar chart

Lecture 285 Python for creating pie chart

Lecture 286 Hands-on: Pie chart

Lecture 287 Python for creating line chart

Lecture 288 Hands-on: Line chart

Lecture 289 Python for creating histogram

Lecture 290 Hands-on: Histogram

Lecture 291 Python for creating Scatterplot

Lecture 292 Hands-on: Scatterplot

Lecture 293 Python for creating Heatmap

Lecture 294 Hands-on: Heatmap

Lecture 295 Python for creating Box-plot

Lecture 296 Hands-on: Box-plot

Lecture 297 Python for creating Kdeplot

Lecture 298 Hands-on: Kdeplot

Lecture 299 Python for Shapiro Wilk test

Lecture 300 Hands-on: Shapiro Wilk test

Lecture 301 Python for data transformation

Lecture 302 Hands-on: Data transformation

Lecture 303 Python for Independent sample t-test

Lecture 304 Hands-on: Independent sample t-test

Lecture 305 Python for Analysis of Variance

Lecture 306 Hands-on: Analysis of Variance

Lecture 307 Python for Chi square test

Lecture 308 Hands-on: Chi square test

Lecture 309 Python for Pearson correlation

Lecture 310 Hands-on: Pearson correlation

Lecture 311 Python for Linear regression

Lecture 312 Hands-on: Linear regression

Lecture 313 Python for generating new features

Lecture 314 Hands-on: Generating new features

Lecture 315 Python for extracting date elements

Lecture 316 Hands-on: Extracting date elements

Lecture 317 Python for encoding feature

Lecture 318 Hands-on: Feature encoding

Lecture 319 Python for binning feature

Lecture 320 Hands-on: Feature binning

Lecture 321 Python for mapping feature

Lecture 322 Hands-on: Feature mapping

Lecture 323 Python for generating dummies

Lecture 324 hands-on: Generating dummies

Lecture 325 Python for Feature selection

Lecture 326 Hand-on: Feature selection

Lecture 327 Python for scaling features

Lecture 328 Hands-on: Feature scaling

Lecture 329 Python for Dimensionality reduction

Lecture 330 Hands-on: Dimensionality reduction

Lecture 331 Python for train-test set

Lecture 332 Hands-on: Train-test set

Lecture 333 Datasets for this phase

Lecture 334 Python for Linear regression model

Lecture 335 Hands-on: Linear regression model

Lecture 336 Python for logistic regression

Lecture 337 Python for logistic regression

Lecture 338 Python for cross-validation

Lecture 339 Hands-on: k-fold cross validation

Lecture 340 Python for regularization

Lecture 341 Hands-on: Model regularization

Lecture 342 Python for oversampling methods

Lecture 343 Hands-on: oversampling methods

Lecture 344 Python for undersampling methods

Lecture 345 Hands-on: Undersampling methods

Lecture 346 Python for KMeans clustering

Lecture 347 Hands-on: KMeans clustering

Lecture 348 Python for decision tree regression

Lecture 349 Hands-on: Decision tree regression

Lecture 350 Python for Decision tree classification

Lecture 351 Hands-on: Decision tree classification

Lecture 352 Python for Random forest regression

Lecture 353 Hands-on: Random forest regression

Lecture 354 Python for Random forest classification

Lecture 355 Hands-on: Random forest classification

Lecture 356 Python for AdaBoost Models

Lecture 357 Hands-on: AdaBoost Models

Lecture 358 Python for Traditional GBM Model

Lecture 359 Hands-on: Traditional GBM Model

Lecture 360 Python for CatBoost Models

Lecture 361 Hands-on: CatBoost Models

Lecture 362 Python for LightGBM Models

Lecture 363 Hands-on: LightGBM Models

Lecture 364 Python for XGBoost Models

Lecture 365 Hands-on: XGBoost Models

Lecture 366 Python for Hyperparameter tuning

Lecture 367 Hands-on: Hyperparameter tuning

Lecture 368 Deep Learning - The data

Lecture 369 Deep Learning - Data Processing

Lecture 370 Deep Learning - Model training

Lecture 371 Deep Learning - Model evaluation

Section 9: Python - Developing AI Projects

Lecture 372 PROJECT 1: Gen-AI Image Captioning

Lecture 373 PROJECT 2: Gen-AI Chatbot

Lecture 374 PROJECT 3: Gen-AI Voice Assistant

Lecture 375 PROJECT 4: Gen-AI Text to Image

Lecture 376 PROJECT 5: Gen-AI Video Summarizer

Lecture 377 PROJECT 6: Gen-AI Language Translator

Lecture 378 PROJECT 7: Gen-AI Data Analyst

Section 10: SQL - Complete Data Analysis and Data Science

Lecture 379 Extra note on functions of MySQL

Lecture 380 RDBMS: example and importance

Lecture 381 Key features of RDBMS

Lecture 382 Primary key v/s Foreign key

Lecture 383 Types of relationship in RDBMS

Lecture 384 Data types in RDBMS

Lecture 385 Introduction to SQL language

Lecture 386 Various platforms of SQL

Lecture 387 Installing MySQL in Windows and Mac

Lecture 388 Download necessary datasets

Lecture 389 Loading CSV dataset in MySQL

Lecture 390 Creating database

Lecture 391 Selecting database

Lecture 392 Modifying database

Lecture 393 Deleting database

Lecture 394 SELECT….FROM: select data from table

Lecture 395 DISTINCT: selecting unique values for column

Lecture 396 AS: selecting columns based on aliases

Lecture 397 WHERE: selecting data based on condition

Lecture 398 CREATE: creating table

Lecture 399 NOT NULL: limiting null values

Lecture 400 UNIQUE: limiting duplicates

Lecture 401 INSERT INTO: adding values in columns

Lecture 402 UPDATE: updating values based on condition

Lecture 403 DELETE: deleting values based on condition

Lecture 404 TRUNCATE: deleting all the values except table

Lecture 405 DROP: removing entire table

Lecture 406 CHECK: limiting specific values in columns

Lecture 407 ADD COLUMN: adding new column

Lecture 408 MODIFY COLUMN: replacing data types

Lecture 409 RENAME COLUMN: changing column names

Lecture 410 DROP COLUMN: deleting columns

Lecture 411 ADD CONSTRAINT: adding primary key

Lecture 412 ADD CONSTRAINT….REFERENCES: adding foreign key

Lecture 413 DROP CONSTRAINT: deleting keys

Lecture 414 CREATE INDEX: creating new index

Lecture 415 CREATE UNIQUE INDEX: creating index without duplicates

Lecture 416 DROP INDEX: deleting existing index

Lecture 417 IS NULL: filtering the actual values out

Lecture 418 IS NOT NULL: filtering the missing values out

Lecture 419 AND: combining two or more conditions

Lecture 420 OR: flexible logical operator

Lecture 421 NOT: excluding values from filteration

Lecture 422 BETWEEN…AND: filtering ranges of values

Lecture 423 LIKE: filtering based on pattern

Lecture 424 IN: precise logic for multiple conditions

Lecture 425 LIMIT: filtering with limited data

Lecture 426 CHAR_LENGTH: finding the length of text

Lecture 427 CONCAT: adding different strings together

Lecture 428 LOWER: converting into lowercase

Lecture 429 UPPER: converting into uppercase

Lecture 430 TRIM: removing unnecessary gaps

Lecture 431 REPLACE: replacing old value by new value

Lecture 432 ABS: negative to positive value

Lecture 433 SUM: calculating the total value

Lecture 434 AVG: calculating the average value

Lecture 435 COUNT: counting total items

Lecture 436 DIV: dividing numeric data

Lecture 437 MIN: finding the lowest value

Lecture 438 MAX: finding the highest value

Lecture 439 POWER: multiple multiplications

Lecture 440 ROUND: decreasing the decimals

Lecture 441 SQRT and LOG: transformation functions

Lecture 442 DATEFORMAT: formatting the date shape

Lecture 443 DATEDIFF: finding the date difference

Lecture 444 DAY/MONTH/YEAR: extracting parts of dates

Lecture 445 ORDER BY: sorting data based on a column

Lecture 446 GROUP BY: group data analysis with functions

Lecture 447 INNER JOIN: joining on common values

Lecture 448 LEFT JOIN: joining on left table values

Lecture 449 RIGHT JOIN: joining on right table values

Lecture 450 CROSS JOIN: joining all values from tables

Lecture 451 HAVING: advanced conditional format

Lecture 452 EXISTS: nested filtering between tables

Lecture 453 ANY: nested filtering between tables

Lecture 454 CASE: finding the conditional outcomes

Lecture 455 SQL comments systems

Lecture 456 Storing and executing procedures

Section 11: PowerBI - Complete Business Intelligence

Lecture 457 Instructions for this Chapter

Lecture 458 Download practice datasets

Lecture 459 Downloading Power BI desktop

Lecture 460 Important setting to Power BI

Lecture 461 Importing dataset into Power BI

Lecture 462 Adjusting table and column names

Lecture 463 Setting correct data types

Lecture 464 Splitting and removing column

Lecture 465 Replacing values in a column

Lecture 466 Text data manipulation

Lecture 467 Numeric data analysis

Lecture 468 Date and time manipulation

Lecture 469 Conditional column

Lecture 470 Grouping and aggregating

Lecture 471 Joining datasets

Lecture 472 Concatenating datasets

Lecture 473 Understanding data modeling

Lecture 474 Creating data model in Power BI

Lecture 475 Managing & editing data models

Lecture 476 Data column formats & categories

Lecture 477 Creating & managing hierarchies

Lecture 478 DAX for math and statistics

Lecture 479 DAX for counting categories

Lecture 480 DAX for logical functions

Lecture 481 Getting started with dashboard

Lecture 482 Installing KPI cards

Lecture 483 Plotting line chart

Lecture 484 Developing area chart

Lecture 485 Installing gauge charts

Lecture 486 Decorating dashboard

Lecture 487 Creating bar charts

Lecture 488 Installing donut chart

Lecture 489 Table matrix visualization

Lecture 490 Map visualization

Lecture 491 SPEACIAL: Power BI tooltip

Lecture 492 SPECIAL: Slicers for interactivity

Lecture 493 SPECIAL: Adding custom button

Lecture 494 IMPORTANT: Final touch

Section 12: Excel - Complete Data Analytics and Statistics

Lecture 495 Extra note on functions and shortcuts

Lecture 496 Identifying and removing duplicates

Lecture 497 Dealing with missing values

Lecture 498 Dealing with outliers

Lecture 499 Finding and imputing inconsistent values

Lecture 500 Text-to-columns for data separation

Lecture 501 Applying sorts & filters to narrow down data

Lecture 502 Advanced filtering with custom criteria

Lecture 503 Highlighting cells based on criteria

Lecture 504 Findings top and bottom insights

Lecture 505 Creating color scales and color bars

Lecture 506 SUM, AVERAGE, MIN, and MAX

Lecture 507 SUMIF, and AVERAGEIF

Lecture 508 COUNT, COUNTA, and COUNTIF

Lecture 509 YEAR, MONTH and DAY

Lecture 510 IF STATEMENTs for conditional operation

Lecture 511 VLOOKUP for column-wise insight search

Lecture 512 HLOOKUP for row-wise insight search

Lecture 513 XLOOKUP for robust & complex insight search

Lecture 514 Analyze data with Stacked and cluster bar charts

Lecture 515 Analyze data with Pie chart and line chart

Lecture 516 Analyze data with Area chart and TreeMap

Lecture 517 Analyze data with Boxplot and Histogram

Lecture 518 Analyze data with Scatter plot and Combo chart

Lecture 519 Adjusting and decorating graphs and charts

Lecture 520 PivotTables for GROUP data analysis

Lecture 521 PivotTables for CROSSTAB analysis

Lecture 522 PivotCharts and Slicers for interactivity

Lecture 523 Descriptive statistics and analysis

Lecture 524 Independent sample t-test for two samples

Lecture 525 Paired sample t-test for two samples

Lecture 526 Analysis of variance – One way ANOVA

Lecture 527 Correlation analysis for relationship

Lecture 528 Multiple linear regression analysis

Lecture 529 Accumulating relevant information

Lecture 530 Creating a canvas for dashboard

Lecture 531 Developing the complete dashboard

Lecture 532 Final touch up for dashboard decoration

Section 13: Capstone Projects - Python, SQL, PowerBI & Excel

Section 14: APPENDIX - ChatGPT for Seamless Data Analytics

Lecture 533 Download practice datasets

Lecture 534 Creating ChatGPT premium account

Lecture 535 Getting Started with GPT-4 Data Analyst

Lecture 536 Data cleaning and manipulation

Lecture 537 Data analysis and hypothesis testing

Lecture 538 Develop machine learning models

Lecture 539 Rapid coding for data analysis

Everyone!