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eMexo Technologies
Data Science
Curriculum
13 Sections
556 Lessons
50 Hours
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Module 1: Introduction to Data Science
In this module of data science training, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
5
1.1
What is Data Science?
1.2
Data Science lifecycle
1.3
Applications of Data Science in different industries
1.4
Data Scientist roles & responsibilities
1.5
Overview of tools & technologies (Python, R, SQL, etc.)
Module 2: Introduction to Python for Data Science
92
2.1
Why Python for Data Science?
2.2
Advantages over other languages (R, Java, etc.)
2.3
Industry use cases
2.4
Python Setup & Environment
2.5
Installing Python (Anaconda, Miniconda)
2.6
Using Jupyter Notebook, Google Colab
2.7
Python interpreters & IDEs (PyCharm, VS Code, Spyder)
2.8
Python Fundamentals
2.9
Python syntax, indentation, and comments
2.10
Variables and data types (int, float, string, boolean)
2.11
Type casting (implicit & explicit)
2.12
Operators
2.13
Arithmetic, comparison, logical, assignment, membership, identity
2.14
Input & output functions
2.15
Hands-on Exercises:
2.16
Creating a basic Python program to take input and display output
2.17
Simple calculator using Python
2.18
Data Structures in Python
2.19
Lists – creation, indexing, slicing, adding/removing elements, list comprehensions
2.20
Tuples – immutability, tuple unpacking
2.21
Sets – uniqueness, set operations (union, intersection, difference)
2.22
Dictionaries – key-value pairs, CRUD operations, looping
2.23
Nested data structures
2.24
Hands-on Exercises:
2.25
Store and retrieve student data using dictionaries
2.26
Unique word extraction from a paragraph
2.27
Control Flow
2.28
Conditional statements – if, elif, else
2.29
Loops – for, while
2.30
Hands-on Exercises:
2.31
Prime number checker
2.32
Pattern printing
2.33
Functions & Modules
2.34
Defining and calling functions
2.35
Function arguments (positional, keyword, default, variable-length)
2.36
Return values
2.37
Lambda functions
2.38
Modules and Packages
2.39
Importing built-in modules (math, datetime, random, os)
2.40
Creating custom modules
2.41
Hands-on Exercises:
2.42
Build a reusable data-cleaning function
2.43
File Handling
2.44
Opening and reading files
2.45
Writing and appending files
2.46
Working with CSV & JSON files
2.47
Hands-on Exercises:
2.48
Read a CSV file and process the data
2.49
Convert JSON data to CSV
2.50
Python Libraries for Data Science
2.51
NumPy – Numerical Computing
2.52
Creating arrays
2.53
Array indexing and slicing
2.54
Array operations
2.55
Mathematical and statistical functions
2.56
Reshaping and stacking arrays
2.57
Broadcasting
2.58
Pandas – Data Manipulation
2.59
Series and DataFrames
2.60
Importing datasets (CSV, Excel, SQL)
2.61
Data selection (loc, iloc)
2.62
Handling missing values
2.63
Data filtering and sorting
2.64
Grouping and aggregation
2.65
Merging and joining DataFrames
2.66
Matplotlib & Seaborn – Data Visualization
2.67
Basic plots (line, bar, scatter, histogram)
2.68
Customizing plots (labels, titles, legends)
2.69
Seaborn visualizations (boxplot, heatmap, pairplot, violin plot)
2.70
Style customization and color palettes
2.71
Data Cleaning & Preprocessing in Python
2.72
Detecting and handling missing data
2.73
Outlier detection and treatment
2.74
Encoding categorical variables
2.75
Feature scaling (normalization, standardization)
2.76
String operations for text cleaning
2.77
Hands-on Exercises:
2.78
Clean and preprocess a dataset for analysis
2.79
Working with APIs & Web Data
2.80
Introduction to APIs
2.81
Using Python’s requests library
2.82
Fetching JSON data from APIs
2.83
Parsing and storing API data
2.84
Hands-on Exercises:
2.85
Fetch live weather data from an API
2.86
Introduction to Statistical Analysis in Python
2.87
Descriptive statistics with Pandas & NumPy
2.88
Correlation & covariance
2.89
Probability distributions (Normal, Binomial)
2.90
Hypothesis testing basics
2.91
Hands-on Exercises:
2.92
Perform statistical summary of a dataset
Module 3: Statistics & Probability for Data Science
74
3.1
Introduction to Statistics for Data Science
3.2
What is Statistics?
3.3
Descriptive vs. Inferential Statistics
3.4
Role of statistics in data science & analytics
3.5
Importance of statistics in decision-making
3.6
Types of data:
3.7
Quantitative vs. Qualitative
3.8
Discrete vs. Continuous
3.9
Nominal, Ordinal, Interval, Ratio scales
3.10
Hands-on: Identify data types from sample datasets
3.11
Descriptive Statistics
3.12
Measures of Central Tendency
3.13
Mean, Median, Mode
3.14
Weighted average
3.15
Measures of Dispersion
3.16
Shape of Data Distribution
3.17
Skewness (positive, negative, zero)
3.18
Kurtosis (leptokurtic, platykurtic, mesokurtic)
3.19
Hands-on: Use Python (Pandas, NumPy) to calculate statistical measures
3.20
Data Visualization for Statistics
3.21
Histograms, Bar Charts, Box Plots
3.22
Scatter Plots for correlation visualization
3.23
Probability distribution plots
3.24
Hands-on: Visualize sales data distribution using Matplotlib/Seaborn
3.25
Probability Fundamentals
3.26
Basic probability concepts
3.27
Sample space, events, outcomes
3.28
Types of probability:
3.29
Theoretical, Experimental, Axiomatic
3.30
Addition & Multiplication rules of probability
3.31
Conditional probability & Independence
3.32
Bayes’ Theorem – theory and real-life applications
3.33
Hands-on: Solve probability problems using Python
3.34
Probability Distributions
3.35
Discrete Distributions
3.36
Bernoulli Distribution
3.37
Binomial Distribution
3.38
Poisson Distribution
3.39
Continuous Distributions
3.40
Uniform Distribution
3.41
Normal (Gaussian) Distribution
3.42
Normal (Gaussian) Distribution
3.43
Empirical rule (68-95-99.7)
3.44
Exponential Distribution
3.45
Hands-on: Plot and simulate different distributions in Python
3.46
Sampling & Sampling Distributions
3.47
Population vs. Sample
3.48
Sampling techniques:
3.49
Random sampling, Stratified sampling, Cluster sampling, Systematic sampling
3.50
Central Limit Theorem & its importance in Data Science
3.51
Sampling distribution of the sample mean
3.52
Hands-on: Simulate Central Limit Theorem using Python
3.53
Inferential Statistics
3.54
Concept of estimation – point & interval estimates
3.55
Confidence intervals (for mean & proportion)
3.56
Margin of error
3.57
Hands-on: Calculate confidence intervals for a dataset
3.58
Hypothesis Testing
3.59
Null Hypothesis (H₀) & Alternative Hypothesis (H₁)
3.60
Type I and Type II errors
3.61
P-value and significance levels (α)
3.62
One-tailed & two-tailed tests
3.63
Common Statistical Tests:
3.64
Z-tes
3.65
T-test (one-sample, independent, paired)
3.66
Chi-Square test
3.67
ANOVA (One-way & Two-way)
3.68
Hands-on: Perform hypothesis testing on sample datasets
3.69
Correlation & Regression Basics
3.70
Covariance & Correlation
3.71
Introduction to Linear Regression
3.72
Interpreting correlation coefficients
3.73
Hands-on: Calculate correlation between features in a dataset
3.74
Real-World Data Science Applications
Module 4: Data Wrangling & Cleaning
5
4.1
Handling missing values
4.2
Data transformations & feature scaling
4.3
Removing duplicates & outliers
4.4
Encoding categorical variables
4.5
Data normalization & standardization
Module 5: Exploratory Data Analysis (EDA)\\
5
5.1
Understanding datasets
5.2
Univariate, bivariate & multivariate analysis
5.3
Visualization techniques (histograms, scatter plots, heatmaps)
5.4
Detecting trends & patterns
5.5
Data summarization & reporting
Module 6: SQL for Data Science
72
6.1
Introduction to SQL & Databases
6.2
What is SQL? Why SQL for Data Science?
6.3
Understanding databases – relational vs. non-relational
6.4
Tables, rows, columns, and relationships
6.5
Primary keys & foreign keys
6.6
Installing & setting up SQL environment (MySQL, PostgreSQL, SQLite)
6.7
Connecting Python to SQL for data analysis
6.8
Hands-on: Create a database and a simple table
6.9
Basic SQL Queries
6.10
SELECT statement
6.11
DISTINCT keyword
6.12
WHERE clause – filtering records
6.13
Logical operators (AND, OR, NOT)
6.14
Comparison operators (=, !=, , =)
6.15
BETWEEN, IN, LIKE (wildcards % and _)
6.16
ORDER BY – sorting results
6.17
LIMIT – restricting output
6.18
Hands-on: Retrieve filtered and sorted data from a dataset
6.19
SQL Functions for Data Analysis
6.20
Aggregate Functions – COUNT(), SUM(), AVG(), MIN(), MAX()
6.21
String Functions – UPPER(), LOWER(), CONCAT(), TRIM(), SUBSTRING()
6.22
Date & Time Functions – NOW(), DATE(), YEAR(), MONTH(), DATEDIFF()
6.23
Mathematical Functions – ROUND(), ABS(), CEIL(), FLOOR()
6.24
Hands-on: Generate sales reports using aggregate functions
6.25
Grouping & Aggregation
6.26
GROUP BY – grouping data for analysis
6.27
HAVING – filtering aggregated data
6.28
Nested aggregation
6.29
Hands-on: Find top-performing products by category
6.30
Joins & Relationships
6.31
INNER JOIN – matching rows between tables
6.32
LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
6.33
Self-joins
6.34
Cross joins
6.35
Joining more than two tables
6.36
Hands-on: Merge customer and sales data for combined analysis
6.37
Subqueries & Derived Tables
6.38
Subqueries in SELECT, FROM, WHERE clauses
6.39
Correlated subqueries
6.40
Derived tables (inline views)
6.41
Hands-on: Find customers who purchased above the average order value
6.42
Set Operations
6.43
UNION and UNION ALL
6.44
INTERSECT
6.45
EXCEPT / MINUS
6.46
Hands-on: Combine datasets from multiple sources
6.47
Data Modification
6.48
INSERT INTO – adding new records
6.49
UPDATE – modifying existing records
6.50
DELETE – removing records
6.51
TRUNCATE – clearing tables
6.52
Hands-on: Update product prices and clean up data
6.53
SQL for Data Cleaning
6.54
Identifying duplicates
6.55
Removing duplicates
6.56
Handling NULL values
6.57
Replacing missing values with default values
6.58
String trimming and formatting
6.59
Hands-on: Clean raw sales data for analysis
6.60
Advanced SQL for Data Science
6.61
Window functions:
6.62
ROW_NUMBER(), RANK(), DENSE_RANK()
6.63
NTILE(), LEAD(), LAG()
6.64
Common Table Expressions (CTEs)
6.65
Pivoting & unpivoting data
6.66
Recursive queries
6.67
Hands-on: Create a monthly revenue trend analysis
6.68
Integrating SQL with Data Science Tools
6.69
Connecting SQL to Python using sqlite3 / SQLAlchemy
6.70
Exporting SQL query results to CSV/Excel
6.71
Using SQL in Jupyter Notebook
6.72
Hands-on: Run SQL queries from Python and visualize results with Matplotlib
Module 7: Machine Learning – Supervised Learning
59
7.1
Introduction to Machine Learning
7.2
What is Machine Learning?
7.3
Definition & key concepts
7.4
Difference between AI, ML, and Deep Learning
7.5
Categories of Machine Learning:
7.6
Supervised, Unsupervised, Reinforcement Learning
7.7
Applications of Supervised Learning in real-world industries
7.8
Overview of the Supervised Learning workflow:
7.9
Data Collection
7.10
Data Preprocessing
7.11
Feature Engineering
7.12
Model Selection
7.13
Training the Model
7.14
Model Evaluation
7.15
Model Deployment
7.16
Linear Regression
7.17
Concept of regression & when to use it
7.18
Simple Linear Regression:
7.19
Equation: 𝑦 = 𝑚 𝑥 + 𝑐 y=mx+c
7.20
Slope & intercept interpretation
7.21
Multiple Linear Regression:
7.22
Handling multiple features
7.23
Assumptions in regression (linearity, independence, homoscedasticity, normality)
7.24
Cost function – Mean Squared Error (MSE)
7.25
Gradient Descent optimization
7.26
Overfitting & underfitting in regression
7.27
Hands-on:
7.28
Build a house price prediction model using Linear Regression
7.29
Evaluate model performance using RMSE & R² score
7.30
Logistic Regression
7.31
Why Logistic Regression for classification problems
7.32
Sigmoid function & probability output
7.33
Decision boundary concept
7.34
Binary classification vs. multi-class classification
7.35
Cost function for logistic regression (log loss)
7.36
Regularization in logistic regression (L1 & L2)
7.37
Decision Trees
7.38
Decision tree basics
7.39
Splitting criteria: Gini Index, Entropy, Information Gain
7.40
Stopping criteria & pruning to avoid overfitting
7.41
Advantages & disadvantages of decision trees
7.42
Random Forest
7.43
Concept of ensemble learning
7.44
Bagging & Random Forest algorithm
7.45
Feature importance in Random Forest
7.46
Hyperparameter tuning (n_estimators, max_depth, max_features)
7.47
Advantages over single decision trees
7.48
K-Nearest Neighbors (KNN)
7.49
Introduction to instance-based learning
7.50
Choosing value of K & effect on bias-variance tradeoff
7.51
Distance metrics: Euclidean, Manhattan, Minkowski
7.52
Scaling features for KNN
7.53
Advantages & limitations
7.54
Model Evaluation Metrics
7.55
Confusion Matrix – TP, FP, TN, FN
7.56
Accuracy, Precision, Recall, F1-score
7.57
ROC Curve & AUC score
7.58
Precision-Recall tradeoff
7.59
Cross-validation & train-test split
Module 8: Machine Learning – Unsupervised Learning
61
8.1
Introduction to Unsupervised Learning
8.2
Definition and key differences from Supervised Learning
8.3
Real-world applications:
8.4
Customer segmentation in marketing
8.5
Anomaly detection in fraud detection
8.6
Document/topic clustering
8.7
Types of unsupervised learning:
8.8
Clustering
8.9
Dimensionality Reduction
8.10
Association Rule Learning
8.11
Workflow of an unsupervised learning project:
8.12
Data collection & preprocessing
8.13
Feature scaling
8.14
Choosing an algorithm
8.15
Model training
8.16
Results interpretation
8.17
Clustering
8.18
K-Means Clustering
8.19
Concept of clustering and distance-based grouping
8.20
How K-Means works:
8.21
Choose K initial centroids
8.22
Assign points to nearest centroid
8.23
Recalculate centroids
8.24
Repeat until convergence
8.25
Choosing optimal K (Elbow method, Silhouette score)
8.26
Advantages & limitations
8.27
Hands-on: Customer segmentation using K-Means
8.28
Hierarchical Clustering
8.29
Agglomerative vs. Divisive clustering
8.30
Dendrograms and linkage criteria (single, complete, average)
8.31
Advantages over K-Means
8.32
When to use hierarchical clustering
8.33
Hands-on: Grouping countries based on socio-economic indicators
8.34
Dimensionality Reduction
8.35
Principal Component Analysis (PCA)
8.36
Why dimensionality reduction is important (curse of dimensionality)
8.37
How PCA works:
8.38
Covariance matrix
8.39
Eigenvalues & eigenvectors
8.40
Principal components
8.41
Variance explained & choosing number of components
8.42
Advantages and trade-offs
8.43
Hands-on: Apply PCA to reduce dimensions in a high-dimensional dataset (e.g., MNIST)
8.44
t-SNE (t-Distributed Stochastic Neighbor Embedding)
8.45
Non-linear dimensionality reduction technique
8.46
Preserving local neighborhood structure
8.47
Key parameters: perplexity, learning rate
8.48
Best practices for visualization
8.49
Hands-on: Visualizing high-dimensional word embeddings
8.50
Association Rule Learning
8.51
Apriori Algorithm
8.52
Market basket analysis concept
8.53
Support, Confidence, Lift metrics
8.54
How Apriori generates frequent itemsets
8.55
Advantages & limitations
8.56
Hands-on: Find frequently bought-together products in retail dataset
8.57
Eclat Algorithm
8.58
Difference from Apriori (depth-first search approach)
8.59
Transaction ID sets and intersection
8.60
Efficiency in sparse datasets
8.61
Hands-on: Implement Eclat for transaction analysis
Module 9: Advanced Machine Learning & Model Optimization
4
9.1
Support Vector Machines (SVM)
9.2
Gradient Boosting (XGBoost, LightGBM, CatBoost)
9.3
Hyperparameter tuning (Grid Search, Random Search)
9.4
Cross-validation
Module 10: Introduction to Deep Learning
39
10.1
Evolution of AI → Machine Learning → Deep Learning
10.2
Why Deep Learning? Handling unstructured data (images, text, audio)
10.3
Key differences between Machine Learning & Deep Learning
10.4
Real-world applications: self-driving cars, healthcare, NLP, image recognition
10.5
Overview of popular deep learning frameworks (TensorFlow, PyTorch, Keras)
10.6
Neural Networks Basics
10.7
Structure of an Artificial Neural Network (ANN): neurons, layers (input, hidden, output)
10.8
Biological inspiration: Neurons and synapses
10.9
Forward propagation: weighted sum, bias, activation
10.10
Backpropagation: gradient descent & optimization
10.11
Overfitting vs underfitting in neural networks
10.12
Hands-on: Build a simple neural network from scratch (using NumPy)
10.13
Activation Functions
10.14
Role of activation functions in neural networks
10.15
Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax – concepts and use cases
10.16
Vanishing gradient & exploding gradient problems
10.17
Choosing the right activation function for classification vs regression tasks
10.18
Hands-on: Experiment with different activations in a simple neural net
10.19
Introduction to TensorFlow & Keras
10.20
Overview of TensorFlow (computational graph, tensors, operations)
10.21
Keras as a high-level API for rapid prototyping
10.22
Installing and setting up TensorFlow/Keras
10.23
Key components: layers, models, optimizers, loss functions
10.24
Hands-on: First deep learning model using Keras Sequential API
10.25
Building a Simple Neural Network
10.26
Steps in building a neural network:
10.27
Data preprocessing and normalization
10.28
Defining input, hidden, and output layers
10.29
Choosing loss function and optimizer
10.30
Training the model and monitoring loss/accuracy
10.31
Evaluating model performance
10.32
Hands-on: Build a neural network to classify MNIST digits
10.33
Image Classification Basics (CNN Concepts)
10.34
Why CNNs for images vs. fully connected networks
10.35
Convolution operation: kernels, filters, strides, padding
10.36
Pooling layers (Max pooling, Average pooling)
10.37
Flattening and fully connected layers in CNNs
10.38
Dropout for regularization
10.39
Hands-on: Build a simple CNN for image classification (MNIST / CIFAR-10 dataset)
Module 11: Data Science Tools & Big Data
53
11.1
1. Jupyter Notebook & Google Colab
11.2
Introduction to Jupyter Notebook
11.3
Features: code cells, markdown, visualization
11.4
Installing and setting up Jupyter Notebook
11.5
Using magic commands (%timeit, %matplotlib inline)
11.6
Exporting notebooks (HTML, PDF, Python scripts)
11.7
Google Colab
11.8
Introduction and benefits (cloud-based execution, free GPU/TPU)
11.9
Creating and managing notebooks on Colab
11.10
Mounting Google Drive for dataset access
11.11
Running deep learning experiments on GPU/TPU
11.12
Collaboration features in Colab
11.13
Hands-on Exercises
11.14
Git & GitHub for Version Control
11.15
Introduction to Version Control
11.16
Why version control is important in Data Science projects
11.17
Git vs GitHub
11.18
Git Basics
11.19
Installing Git
11.20
Core commands: git init, git add, git commit, git push, git pull
11.21
Branching and merging
11.22
GitHub Essentials
11.23
Creating and managing repositories
11.24
Cloning repositories and pushing local changes
11.25
Pull requests, code reviews, and collaboration
11.26
GitHub Actions (Intro to automation & CI/CD for ML projects)
11.27
Hands-on Exercises
11.28
Big Data Concepts & Hadoop Basics
11.29
Introduction to Big Data
11.30
What is Big Data? 5Vs of Big Data (Volume, Velocity, Variety, Veracity, Value)
11.31
Challenges with traditional data processing
11.32
Big Data in the Data Science ecosystem
11.33
Hadoop Framework
11.34
Hadoop ecosystem components: HDFS, YARN, MapReduce
11.35
HDFS (Hadoop Distributed File System) – storage architecture
11.36
MapReduce basics – processing large datasets in parallel
11.37
Introduction to Hive and Pig (high-level querying tools)
11.38
Use Cases
11.39
Big Data in healthcare, finance, e-commerce, social media
11.40
Hands-on Exercises
11.41
Introduction to Apache Spark for Data Science
11.42
Why Spark?
11.43
Limitations of Hadoop MapReduce
11.44
Advantages of Apache Spark (speed, in-memory processing, ease of use)
11.45
Spark Basics
11.46
Spark architecture: RDDs, DataFrames, DAG execution engine
11.47
Spark ecosystem: Spark SQL, Spark MLlib, Spark Streaming, GraphX
11.48
PySpark – using Spark with Python
11.49
Data Science with Spark
11.50
Loading and exploring datasets in Spark
11.51
Data transformations and actions
11.52
Integrating Spark with MLlib for machine learning
11.53
Hands-on Exercises
Module 12: Power BI
85
12.1
Introduction to Power BI
12.2
Get Started with Power BI
12.3
Overview: Power BI concepts
12.4
Sign up for Power BI
12.5
Overview: Power BI data sources
12.6
Connect to a SaaS solution
12.7
Upload a local CSV file
12.8
Connect to Excel data that can be refreshed
12.9
Connect to a sample
12.10
Create a Report with Visualizations
12.11
Create a Report with Visualizations
12.12
Hands-On
12.13
Viz and Tiles
12.14
Overview: Visualizations
12.15
Using visualizations
12.16
Create a new report
12.17
Create and arrange visualizations
12.18
Format a visualization
12.19
Create chart visualizations
12.20
Use text, map, and gauge visualizations and save a report
12.21
Use a slicer to filter visualizations
12.22
Sort, copy, and paste visualizations
12.23
Download and use a custom visual from the gallery
12.24
Hands-On
12.25
Reports and Dashboards
12.26
Modify and Print a Report
12.27
Rename and delete report pages
12.28
Add a filter to a page or report
12.29
Set visualization interactions
12.30
Print a report page
12.31
Send a report to PowerPoint
12.32
Create a Dashboard
12.33
Create and manage dashboards
12.34
Pin a report tile to a dashboard
12.35
Pin a live report page to a dashboard
12.36
Pin a tile from another dashboard
12.37
Pin an Excel element to dashboard
12.38
Manage pinned elements in Excel
12.39
Manage pinned elements in Excel
12.40
Add a tile to a dashboard
12.41
Build a dashboard with Quick Insights
12.42
Set a Featured (default) dashboard
12.43
Ask Questions about Your Data
12.44
Ask Questions about Your Data
12.45
Tweak your dataset for Q&A
12.46
Enable Cortana for Power BI
12.47
Hands-On
12.48
Publishing Workbooks and Workspace
12.49
Share Data with Colleagues and Others
12.50
Share Data with Colleagues and Others
12.51
Publish a report to the web
12.52
Manage published reports
12.53
Share a dashboard
12.54
Create an app workspace and add users
12.55
Use an app workspace
12.56
Publish an app
12.57
Create a QR code to share a tile
12.58
Embed a report in SharePoint Online
12.59
Hands-On
12.60
Other Power BI Components and Table Relationship
12.61
Use Power BI Mobile Apps
12.62
Get Power BI for mobile
12.63
View reports and dashboards in the iPad app
12.64
Use workspaces in the mobile app
12.65
Sharing from Power BI Mobile
12.66
Use Power BI Desktop
12.67
Install and launch Power BI Desktop
12.68
Get data
12.69
Reduce data
12.70
Transform data
12.71
Relate tables
12.72
Get Power BI Desktop data with the Power BI service
12.73
Export a report from Power BI service to Desktop
12.74
Hands-On
12.75
DAX functions
12.76
New Dax functions
12.77
Date and time functions
12.78
Time intelligence functions
12.79
Filter functions
12.80
Information functions
12.81
Logical functions
12.82
Math & trig functions
12.83
Parent and child functions
12.84
Text functions
12.85
Hands-On
Module 13: Artificial Intelligence (AI)
2
13.1
Introduction to Artificial Intelligence
13.2
What is Artificial Intelligence?
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