Curriculum
10 Sections
348 Lessons
44 Weeks
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Data Analysis in Excel
38
1.0
QUICK REVIEW ON MS EXCEL OPTIONS, RIBBON, SHEETS
1.1
SAVING EXCEL FILE AS PDF, CSV AND OLDER VERSIONS
1.2
USING EXCEL SHORTCUTS WITH FULL LIST OF SHORTCUTS
1.3
COPY, CUT, PASTE, HIDE, UNHIDE, AND LINK THE DATA IN ROWS, COLUMNS AND SHEET
1.4
USING PASTE SPECIAL OPTIONS FORMATTING CELLS, ROWS, COLUMNS AND SHEETS
1.5
DATA VALIDATION IN EXCEL
1.6
ADVANCED FILTERING IN EXCEL
1.7
GROUPING & SUBTOTAL IN EXCEL
1.8
PROTECTING & UNPROTECTING CELLS, ROWS, COLUMNS AND SHEETS WITH OR WITHOUT PASSWORD
1.9
PAGE LAYOUT AND PRINTER PROPERTIES
1.10
WORKING WITH FORMULAS / FUNCTION
1.11
LOOKUP AND REFERENCE FUNCTIONS: VLOOKUP, HLOOKUP, INDEX, MATCH, ETC
1.12
LOGICAL FUNCTION: IF / ELSE, AND, OR, NOT, TRUE, NESTED IF/ELSE ETC
1.13
DATE & TIME FUNCTIONS: DATE, DATEVALUE, DAY, DAY360, SECOND, MINUTES, HOURS, NOW, TODAY MONTH, YEAR, YEARFRAC, TIME, WEEKDAY, WORKDAY
1.14
MATH & TRIGONOMETRY FUNCTIONS: RAND, ROUND, CEILING, FLOOR, INT, LCM, EVEN, SUMIF
1.15
STATISTICAL FUNCTIONS: AVEDEV, AVERAGE, AVERAGEA, AVERAGEIF, COUNT, COUNTA, COUNTBLANK, COUNTIF, MAX, MAXA,MIN, MINA, STDEVA
1.16
TEXT FUNCTIONS: LEFT, RIGHT, TEXT, TRIM, MID, LOWER, UPPER, PROPER, REPLACE, REPT, FIND, SEARCH, SUBSTITUTE, TRIM, TRUNC, CONVERT, CONCATENATE.
1.17
USING CONDITIONAL FORMATTING WITH MULTIPLE CELL RULES
1.18
USING COLOR SCALES AND ICON SETS IN CONDITIONAL FORMATTING
1.19
CREATING NEW RULES AND MANAGING EXISTING RULES
1.20
DATA LOCK & PROTECTION
1.21
ADVANCE CHARTS IN EXCEL
1.22
AREA CHARTS AND SURFACE CHARTS
1.23
TREND LINE CHARTS AND CANDLE STICK
1.24
XY (SCATTER CHARTS)
1.25
TIME SERIES CHARTS AND BUBBLE CHARTS
1.26
RADAR CHARTS AND DOUGHNUT CHARTS
1.27
WORKING WITH FILL SERIES & GO TO SPECIAL
1.28
CONSOLIDATION IN EXCEL
1.29
GOAL SEEK & SCENARIO
1.30
MANAGER & DATA TABLE
1.31
PROFESSIONAL DASHBOARDS IN EXCEL
1.32
INTERACTIVE SALES DASHBOARD IN EXCEL
1.33
BANK & MARKETING DASHBOARD
1.34
INTERACTIVE HR DASHBOARD IN EXCEL
1.35
PROJECT MANAGEMENT DASHBOARD IN EXCEL
1.36
CALL CENTRE DASHBOARD IN EXCEL
1.37
WORKING WITH HISTOGRAM
Database Design with MySQL
21
2.0
INTRODUCTION
2.1
WHAT IS A DATA WAREHOUSE?
2.2
ENTITY CONSTRAINTS
2.3
DDL AND DML STATEMENTS
2.4
QUERYING IN MYSQL
2.5
SQL STATEMENTS AND OPERATORS
2.6
AGGREGATE FUNCTIONS
2.7
ORDERING
2.8
STRING AND DATE-TIME FUNCTIONS
2.9
REGULAR EXPRESSIONS
2.10
NESTED QUERIES
2.11
VIEWS in SQL
2.12
JOINS AND SET OPERATIONS
2.13
SET THEORY
2.14
TYPES OF JOINS
2.15
TYPES OF JOINS: A DEMONSTRATION
2.16
OUTER JOINS: A DEMONSTRATION
2.17
VIEWS WITH JOINS
2.18
SET OPERATIONS WITH SQL
2.19
ASSIGNMENT
2.20
CASE STUDY
DATA ANALYSIS IN PYTHON
41
3.0
NUMPY IN PYTHON
3.1
INTRODUCTION TO NUMPY
3.2
BASICS OF NUMPY
3.3
MATHEMATICAL OPERATIONS ON NUMPY
3.4
MATHEMATICAL OPERATIONS ON NUMPY II
3.5
CREATING NUMPY ARRAYS
3.6
OPERATIONS OVER 1-D ARRAYS
3.7
MULTIDIMENSIONAL ARRAYS
3.8
COMPUTATION TIMES IN NUMPY VS PYTHON
3.9
PANDAS IN PYTHON
3.10
INTRODUCTION TO PANDAS
3.11
BASICS OF PANDAS
3.12
PANDAS – ROWS AND COLUMNS
3.13
DESCRIBING DATA
3.14
INDEXING AND SLICING
3.15
OPERATIONS ON DATA FRAMES
3.16
GROUP AND AGGREGATE FUNCTIONS
3.17
MERGING DATA FRAMES
3.18
DATA VISUALISATION IN PYTHON
3.19
INTRODUCTION TO DATA VISUALISATION WITH MATPLOTLIB
3.20
THE NECESSITY OF DATA VISUALISATION VISUALISATIONS – SOME EXAMPLES
3.21
FACTS AND DIMENSIONS
3.22
BAR GRAPH
3.23
SCATTER PLOT
3.24
LINE GRAPH AND HISTOGRAM
3.25
OUTLIERS ANALYSIS WITH BOXPLOTS
3.26
SUBPLOTS
3.27
CHOOSING PLOT TYPES
3.28
PROJECTS
3.29
DATA VISUALISATION IN PYTHON WITH SEABORN
3.30
INTRODUCTION
3.31
DISTRIBUTION PLOTS & STYLING OPTIONS
3.32
PIE – CHART AND BAR CHART
3.33
SCATTER PLOTS & PAIR PLOTS
3.34
REVISITING BAR GRAPHS AND BOX PLOTS
3.35
HEATMAPS
3.36
LINE CHARTS
3.37
STACKED BAR CHARTS
3.38
CASE STUDY SUMMARY
3.39
DATA VISUALISATION PRACTICE
3.40
CASE STUDY
Statistics Essentials
10
4.0
MEAN, MEDIAN, MODE STANDARD DEVIATION, VARIANCE, STANDARD ERROR
4.1
CO-RELATION & COEFFICIENT
4.2
PROBABILITY
4.3
REGRESSION ANALYSIS
4.4
MULTIPLE REGRESSION
4.5
LOGISTIC REGRESSION
4.6
TIME SERIES
4.7
NORMAL DISTRIBUTION
4.8
BINOMIAL DISTRIBUTION
4.9
ASSIGNMENT
R Programming
30
5.0
R–Overview
5.1
R – Environment Setup
5.2
R – Basic Syntax
5.3
R – Data Types
5.4
R – Variables
5.5
R – Operators
5.6
R – Decision Making
5.7
R – Loops & R – Function
5.8
R – Strings & R – Lists
5.9
R – Matrices
5.10
R – Arrays & R – Factors
5.11
R – Data Frames
5.12
R – Packages
5.13
R – Data Reshaping Melt The Data
5.14
R – Csv Files
5.15
R – Excel File Input As Xlsx
5.16
R – Binary Files
5.17
R – Xml Files
5.18
R – Json File
5.19
R – Web Data
5.20
R – Databases
5.21
R – Pie Charts & R – Bar Charts
5.22
R – Boxplots & R – Histograms
5.23
R – Scatterplots
5.24
R – Mean, Median & Mode
5.25
R – Linear Regression
5.26
R – Multiple Regression
5.27
R – Logistic Regression
5.28
R – Normal Distribution
5.29
R – Binomial Distribution
Microsoft Power BI
29
6.1
COMPONENTS OF POWER BI: DESKTOP, SERVICE, AND MOBILE APPS
6.2
BENEFITS AND APPLICATIONS OF POWER BI
6.3
POWER BI ARCHITECTURE OVERVIEW
6.4
INSTALLING AND SETTING UP POWER BI DESKTOP
6.5
CONNECTING TO DATA SOURCES (EXCEL, CSV, ONLINE SERVICES, ETC.)
6.6
CLEANING AND TRANSFORMING DATA
6.7
HANDLING MISSING DATA SPLITTING AND MERGING COLUMNS
6.8
DATA FORMATTING AND STANDARDIZATION WORKING WITH RELATIONSHIPS BETWEEN TABLES
6.9
CREATING CUSTOM COLUMNS AND MEASURES WITH DAX (DATA ANALYSIS EXPRESSIONS)
6.10
CREATING RELATIONSHIPS BETWEEN TABLES UNDERSTANDING
6.11
CALCULATED COLUMNS AND MEASURES
6.12
MANAGING MODEL PERFORMANCE WITH OPTIMIZATION TECHNIQUES
6.13
CREATING BASIC CHARTS: BAR, LINE, PIE, AND COLUMN CHARTS
6.14
ADVANCED VISUALS: SCATTER PLOTS, MAPS, FUNNEL CHARTS, & GAUGES USING SLICERS, FILTERS, & DRILL-THROUGHS.
6.15
CREATING AND CUSTOMIZING DASHBOARDS FORMATTING AND STYLING REPORTS FOR BETTER USER EXPERIENCE USING POWER BI MARKETPLACE FOR CUSTOM VISUALS
6.16
CALCULATED COLUMNS AND MEASURES
6.17
AGGREGATION FUNCTIONS (SUM, AVERAGE, COUNT)
6.18
TIME INTELLIGENCE FUNCTIONS (YTD, MTD, QTD)
6.19
PUBLISHING REPORTS TO POWER BI SERVICE
6.20
CREATING DASHBOARDS IN POWER BI SERVICE
6.21
SHARING REPORTS AND DASHBOARDS WITH OTHERS
6.22
EXPORTING REPORTS TO PDF
6.23
SALES ANALYSIS
6.24
FINANCIAL REPORTING
6.25
CONNECTING POWER BI DESKTOP WITH MOBILE FOR REAL TIME ANALYSIS
6.26
RETAIL ANALYSIS DASHBOARDS
6.27
TELECOM CHURN RATE DASHBOARD
6.28
OLA COMPANY DASHBOARD
6.29
BLINKIT ANALYSIS DASHBOARD
Understanding EDA (THE CRISP-DM FRAMEWORK)
33
7.0
DEFINE THE BUSINESS PROBLEM – BUSINESS
7.1
PUBLIC AND PRIVATE DATA
7.2
DATA SOURCING
7.3
UNDERSTANDING RAW DATA
7.4
DATA CLEANING
7.5
FILTERING DATA
7.6
FIXING ROWS AND COLUMNS
7.7
STANDARDISING VALUES
7.8
MISSING VALUES
7.9
INVALID VALUES
7.10
INTRODUCTION TO EDA
7.11
PREPARING DATA FOR ANALYSIS
7.12
DATA ANALYSIS: MODELLING
7.13
MODEL EVALUATION
7.14
MODEL DEPLOYMENT
7.15
ASSIGNMENT
7.16
UNIVARIATE ANALYSIS
7.17
UNORDERED CATEGORICAL VARIABLES
7.18
QUANTITATIVE VARIABLES – UNIVARIATE ANALYSIS
7.19
QUANTITATIVE VARIABLES – SUMMARY METRICS
7.20
SEGMENTED UNIVARIATE
7.21
BASIS OF SEGMENTATION
7.22
QUICK WAY OF SEGMENTATION
7.23
COMPARISON OF AVERAGES
7.24
COMPARISON OF OTHER METRICS
7.25
BIVARIATE ANALYSIS
7.26
BIVARIATE ANALYSIS ON CONTINUOUS VARIABLES
7.28
BUSINESS PROBLEMS INVOLVING CORRELATION
7.29
BIVARIATE ANALYSIS ON CATEGORICAL VARIABLES
7.30
DERIVED METRICS
7.31
TYPES DRIVEN METRICS
7.32
BUSINESS DRIVEN METRICS
7.33
DATA DRIVEN METRICS
EDA Projects & Case Study
19
8.0
INDUSTRIAL CASE STUDIES
8.1
CASE STUDY ON FORMATTING THE DATA
8.2
COST CALCULATION CASE STUDY
8.3
CASE STUDY ON SHOE COMPANY
8.4
DATA FILTERING MINI CHALLENGE
8.5
ADVANCED CHARTS CASE STUDY
8.6
DATA CLEANING & PREAPRING IN EXCEL
8.7
ADVANCE PIVOT BASED REPORT GENERATION
8.8
MAKING REPORT ON HOTEL BOOKING
8.9
SALES CASE STUDY FOR A TOY COMPANY
8.10
EDA PROJECTS
8.11
PROJECT ON DRUG REVIEWS DATASET
8.12
PROJECT ON IPL / FOOTBALL DATA ANALYSES
8.13
PROJECT ON EXPLORING FACTORS OF LIFE
8.14
EXPECTANCY
8.15
PROJECT ON TIME SERIES FORECAST ON ENERGY CONSUMPTION
8.16
PROJECT ON LOAN PREDICTION
8.17
PROJECT ON HOME PRICE PREDICTION
8.18
PROJECT ON OTT ( NETFLIX / HOTSTAR DISNEY )
DATA SCIENCE AND AI
60
9.0
ANALYTICS PROBLEM SOLVING
9.1
PYTHON FOR DATA SCIENCE
9.2
HYPOTHESIS TESTING
9.3
CONCEPTS OF HYPOTHESIS TESTING – I: NULL AND ALTERNATE HYPOTHESIS, MAKING A DECISION, AND CRITICAL VALUE METHOD
9.4
CONCEPTS OF HYPOTHESIS TESTING – II: P VALUE METHOD AND TYPES OF ERRORS
9.5
INDUSTRY DEMONSTRATION OF HYPOTHESIS TESTING: TWO-SAMPLE MEAN AND PROPORTION TEST, A/B TESTING
9.6
LINEAR REGRESSION
9.7
SIMPLE LINEAR REGRESSION
9.8
SIMPLE LINEAR REGRESSION IN PYTHON
9.9
MULTIPLE LINEAR REGRESSION
9.10
MULTIPLE LINEAR REGRESSION IN PYTHON
9.11
INDUSTRY RELEVANCE OF LINEAR REGRESSION
9.12
LOGISTIC REGRESSION
9.13
UNIVARIATE LOGISTIC REGRESSION
9.14
MULTIVARIATE LOGISTIC REGRESSION:
9.15
MODEL BUILDING AND EVALUATION
9.16
LOGISTIC REGRESSION: INDUSTRY APPLICATIONS
9.17
CLASSIFICATION USING DECISION TREES
9.18
INTRODUCTION TO DECISION TREES
9.19
HYPERPARAMETER TUNING IN DECISION TREES
9.20
ALGORITHMS FOR DECISION TREES CONSTRUCTION
9.21
UNSUPERVISED LEARNING
9.22
CLUSTERING
9.23
INTRODUCTION TO CLUSTERING
9.24
K-MEANS CLUSTERING
9.25
HIERARCHICAL CLUSTERING
9.26
OTHER FORMS OF CLUSTERING: K-MODE, K-PROTOTYPE, DB SCAN
9.27
BASICS OF NLP AND TEXT MINING
9.28
REGEX AND INTRODUCTION TO NLP
9.29
BASIC LEXICAL PROCESSING
9.30
ADVANCED LEXICAL PROCESSING
9.31
BUSINESS PROBLEM SOLVING
9.32
INTRODUCTION TO BUSINESS PROBLEM SOLVING
9.33
BUSINESS PROBLEM SOLVING: CASE STUDY DEMONSTRATIONS
9.34
Learn how to approach open ended, real- world problems using data as a lever to draw actionable insights.
9.35
CASE STUDY: LEAD SCORING
9.36
PROBLEM STATEMENT
9.37
INTRODUCTION TO RANDOM FORESTS
9.38
HIERARCHICAL CLUSTERING
9.39
FEATURE IMPORTANCE IN RANDOM FORESTS
9.40
RANDOM FORESTS IN PYTHON
9.41
Learn how powerful ensemble algorithms can improve your classification models by building random forests from decision trees
9.42
BOOSTING
9.43
INTRODUCTION TO BOOSTING AND ADABOOST
9.44
GRADIENT BOOSTING
9.45
PRINCIPAL COMPONENT ANALYSIS
9.46
SINGULAR VALUE DECOMPOSITION
9.47
ADVANCED REGRESSION
9.48
GENERALISED LINEAR REGRESSION
9.49
REGULARISED REGRESSION
9.50
ADVANCED ML CASE STUDY
9.51
MODEL SELECTION & GENERAL ML TECHNIQUES
9.52
PRINCIPLES OF MODEL SELECTION
9.53
MODEL EVALUATION
9.54
MODEL SELECTION: BEST PRACTICES
9.55
TIME SERIES FORECASTING
9.56
INTRODUCTION TO TIME SERIES AND ITS COMPONENTS
9.57
SMOOTHING TECHNIQUES
9.58
INTRODUCTION TO AR MODELS
9.59
BUILDING AR MODELS
Natural Language Processing (NLP)
67
10.0
FUNDAMENTALS OF NLP
10.1
Text Preprocessing: Techniques for cleaning and preparing text (tokenization, stemming, lemmatization, stop-word removal)
10.2
Text Representation: Converting words to numerical representations (Bag of Words, TF-IDF, Word Embeddings).
10.3
WORD EMBEDDINGS
10.4
Word2Vec, GloVe, and FastText: Learn how words are embedded into vector space, capturing semantic meaning.
10.5
Contextual Embeddings: Explore advanced methods like ELMo, BERT, and GPT, which consider the context of words.
10.6
SEQUENCE MODELS
10.7
RNNs (Recurrent Neural Networks): Learn how RNNs handle sequential data such as sentences.
10.8
LSTMs and GRUs: Explore advanced RNN models for better handling long-range dependencies in text.
10.9
ATTENTION MECHANISMS & TRANSFORMERS
10.10
Attention Mechanism: Understand how attention models focus on relevant parts of a sentence.
10.11
Transformers: Learn the foundation of models like BERT and GPT that revolutionized NLP with non sequential processing.
10.12
LANGUAGE MODELS
10.13
Pretrained Models (BERT, GPT, etc.): Study large models that are pretrained on vast corpora, allowing for fine-tuning on specific NLP tasks.
10.14
Transfer Learning: Using pretrained models for specific applications like text classification or translation.
10.15
TEXT GENERATION
10.16
Sequence-to-Sequence Models: Understand how models generate text, such as in translation, summarization, or chatbot conversations.
10.17
Generative Models: Explore GPT and similar models for generating human-like text.
10.18
SENTIMENT ANALYSIS AND TEXT CLASSIFICATION
10.19
Sentiment Analysis: Techniques for analyzing text for emotions or opinions.
10.20
Text Classification: Methods for categorizing text into predefined classes (e.g., spam detection, topic classification).
10.21
NAMED ENTITY RECOGNITION
10.22
(NER) AND PART-OF-SPEECH
10.23
(POS) TAGGING
10.24
NER: Extract important entities (people, places, dates) from text.
10.25
POS Tagging: Assign grammatical categories (nouns, verbs) to words in a sentence.
10.26
SPEECH AND TEXT SYNTHESIS
10.27
Text-to-Speech (TTS): Convert written text into spoken words.
10.28
Speech Recognition: Use models to convert spoken language into written text.
10.29
DEEP LEARNING
10.30
NEURAL NETWORKS
10.31
FUNDAMENTALS
10.32
Perceptrons and Multilayer Networks: Basics of neural networks, how neurons work, and the architecture of feedforward neural networks (FNNs).
10.33
Activation Functions: Explore functions like ReLU, Sigmoid, and Tanh, used to introduce non linearity in neural networks.
10.34
BACKPROPAGATION AND OPTIMIZATION
10.35
Gradient Descent: Learn how neural networks are trained using backpropagation and optimization techniques like stochastic gradient descent (SGD) and Adam.
10.36
Loss Functions: Study different loss functions such as mean squared error (MSE) and cross-entropy for classification and regression tasks.
10.37
DEEP LEARNING
10.38
DEEP LEARNING ARCHITECTURES
10.39
Fully Connected Networks (FNNs): Learn about the structure of deep networks where each neuron connects to every neuron in the next layer.
10.40
Convolutional Neural Networks (CNNs): Understand CNNs for image recognition, object detection, and computer vision tasks.
10.41
Recurrent Neural Networks (RNNs): Study RNNs and their variants (LSTM, GRU) for handling sequential data like time series or text.
10.42
REGULARIZATION TECHNIQUES
10.43
Overfitting Solutions: Learn techniques like dropout, L2 regularization, and batch normalization to improve generalization and prevent overfitting.
10.44
DEEP LEARNING
10.45
TRANSFER LEARNING
10.46
Pretrained Models: Utilize pre trained models (e.g., VGG, ResNet, BERT) and fine-tune them for specific tasks, saving time and computational resources.
10.47
AUTOENCODERS AND REPRESENTATION LEARNING
10.48
Autoencoders: Learn how autoencoders compress data into lower-dimensional representations for tasks like anomaly detection and unsupervised learning.
10.49
SEQUENCE MODELS AND ATTENTION
10.50
RNNs, LSTMs, GRUs: Explore models for sequential data like time-series prediction, language translation, and speech recognition.
10.51
Attention Mechanism: Learn how attention is used in sequence models to focus on important parts of input data.
10.52
Transformers: Study transformer architecture for handling long-range dependencies, the backbone of models like BERT and GPT.
10.53
DEEP LEARNING
10.54
GENERATIVE MODELS
10.55
Generative Adversarial Networks (GANs): Understand how GANs generate new data similar to a given dataset, such as generating realistic images.
10.56
Variational Autoencoders (VAEs): Learn VAEs for probabilistic modeling and generating new, high quality data samples.
10.57
COMPUTER VISION
10.58
Image Processing with CNNs: Use CNNs for image classification, object detection, and segmentation tasks.
10.59
Advanced Vision Techniques: Learn transfer learning, data augmentation, and fine-tuning for improving model performance on vision tasks.
10.60
Text Processing: Study deep learning models for text-based applications like language modeling, text classification, sentiment analysis, and machine translation.
10.61
REINFORCEMENT LEARNING
10.62
RL Algorithms: Understand how deep learning is used in reinforcement learning for decision making and optimizing tasks over time, such as in games or robotics.
10.63
DEPLOYING AND SCALING DEEP
10.64
LEARNING MODELS
10.65
Model Deployment: Learn how to deploy trained deep learning models for real-world applications using frameworks like TensorFlow, PyTorch, and ONNX.
10.66
Scaling and Optimization: Use techniques for distributed training and model optimization to handle large datasets and improve inference speed.
Advanced Certificate in Data Analysis, Data Science & AI
Curriculum
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