README.md

day by day to be a Data Scientist

roadmap

First check this article on medium it will provide you will a lot of information: here

DAY 1 :

Topic: Learn fundamentals of Python:

• Variables
• Numbers
• Strings
• Lists
• Dictionaries
• Sets
• Tuples
• Control Structures
  • If conditions
  • For loops

Resource: Exploring The Power of python:part 1

DAY 2 :

Topic: Advance Python:

• Functions
  • Lambda Functions
  • Module Management (pip install)
  • File Operations (read/write)
  • Object-Oriented Programming
  • Classes
  • Objects

Resources: Exploring the power of python:part 2

Note: first, you need to accomplish the project and then compare it with the given solution

Project: [Personal Finance Tracker and Budget Planner](Project: Personal Finance Tracker and Budget Planner)

Solution: Solution of the Project

Additional resource: all the builtins functions

Day 3:

Topic: Numpy

1. Numpy Array Basics
2. Array Inspection
3. Array Operations
4. Working with Numpy Arrays
5. NumPy for Data Cleaning
6. NumPy for Statistical Analysis
7. Advanced NumPy Techniques

Resources: Numpy Medium

Project MovieLens Project

Solution Solution of the Project

Additional resource summary about Numpy

DAY 4:

Topic: Pandas

1. What is Pandas
2. Installation and Setup
   • How to install Pandas
   • Setting up the environment
3. Pandas Data Structures
   • Series: Basics and creation
   • DataFrame: Basics, creation, and operations
4. Data Importing and Exporting
   • Reading data from different sources (CSV, Excel, etc.)
   • Writing data to files
5. Basic Data Operations
   • Data selection and filtering
   • Data sorting
   • Handling missing values
6. Data Aggregation and Grouping
   • Group by operations
   • Aggregate functions (sum, mean, etc.)

Resource Pandas part1

DAY 5:

Topic:Intermediate Pandas

1. Advanced Data Selection
2. Data Transformation
3. Time Series Analysis in Pandas
4. Performance Enhancement Techniques

resources Pandas part 2:

Project Description of the project

Solution Kaggle

Additional Source time and categorical data

DAY 6 :

Topic: Matplotlib Fundamentals

1. Basic Plotting
2. Plot Types
   • 2.1 Bar Chart
   • 2.2 Histograms
   • 2.3 Scatter plots
   • 2.4 Pie Charts
   • 2.5 Box Plot (Box and Whisker Plot)
   • 2.6 Heatmap
   • 2.7 Stack Plot

DAY 7:

Topic: Advanced Matplotlib

1. Multiple Subplots
   • 1.1 Creating Multiple Plots in a Single Figure
   • 1.2 Combining Different Types of Plots
2. Advanced Features
   • 2.1 Adding annotations and text
   • 2.2 Fill the Area Between Plots
   • 2.3 Plotting Time Series Data
   • 2.4 Creating 3D Plots
   • 2.5 Live Plot - Incorporating Animations and Interactivity.

Aditional resource Articel

Aditional resource jupyter Notebook: This file contains a wide range of techniques for better visualization.

DAY 8 :

Topic: Seaborn part 1:

• scatter plots
• line plots
• bar plots
• histograms
• density plots
• box plots
• violin plots
• heatmaps

resource datacamp article

DAY 9:

Topic: Seaborn Part 2:

• pair plots
• joint plots
• facet grids
• Customizing Seaborn plots
  • Changing Color Palettes
  • Adjusting Figure Size
  • Adding Annotations

resource datacamp article

DAY 10:

Topic: Seaborn Part 3:

Today, we will explore various probability distributions and their visualizations using the Seaborn library.

• Normal Distribution
• Binomial Distribution
• Poisson Distribution
• Uniform Distribution
• Logistic Distribution
• Multinomial Distribution
• Exponential Distribution
• Chi Square Distribution
• Rayleigh Distribution
• Pareto Distribution
• Zipf Distribution

resource w3scole article

Description here

Project jupyter NoteBook

DAY 11:

Topic: Data Cleaning

1. Data Inspection.
2. Handling missing values.
3. Data Imputation

We will discover all this throut this project:

Discription

Solution

DAY 12:

Topic: Statistique and Probability part 1:

• Unit 1: Analyzing Categorical Data

  • Topics include analyzing one categorical variable, two-way tables, distributions in two-way tables.

• Unit 2: Displaying and Comparing Quantitative Data

  • Covers displaying quantitative data with graphs, describing and comparing distributions, and more on data displays.

• Unit 3: Summarizing Quantitative Data

  • Focuses on measuring center in quantitative data, interquartile range, variance, and standard deviation.

• Unit 4: Modeling Data Distributions

  • Includes topics like percentiles, z-scores, density curves, and normal distributions.

Resource:

DAY 13:

Topic: Statistique and Probability part 2:

• Unit 5: Exploring Bivariate Numerical Data

  • Discusses scatterplots, correlation coefficients, trend lines, and regression.

• Unit 6: Study Design

  • Covers statistical questions, sampling methods, types of studies, and experiments.

• Unit 7: Probability

  • Topics include theoretical probability, set operations, experimental probability, and rules of probability.

• Unit 8: Counting, Permutations, and Combinations

  • Focuses on counting principle, permutations, combinations, and combinatorics.

Resource:

DAY 14:

Topic: Statistique and Probability part 3:

• Unit 9: Random Variables

  • Discusses discrete and continuous random variables, transforming and combining random variables, binomial and geometric distributions, and more.

• Unit 10: Sampling Distributions

  • Covers the concept of sampling distributions, including distributions of sample proportions and means.

• Unit 11: Confidence Intervals

  • Introduces confidence intervals and covers how to estimate population proportions and means.

• Unit 12: Significance Tests (Hypothesis Testing)

  • Explores the idea of significance tests, error probabilities, tests about population proportions and means, and more.

Resource:

DAY 15:

Topic: Statistique and Probability part 4:

• Unit 13: Two-Sample Inference for the Difference Between Groups

  • Focuses on comparing two proportions and two means, among other related topics.

• Unit 14: Inference for Categorical Data (Chi-Square Tests)

  • Discusses chi-square goodness-of-fit tests and chi-square tests for relationships.

• Unit 15: Advanced Regression (Inference and Transforming)

  • Covers inference about slope, nonlinear regression, and other advanced regression topics.

• Unit 16: Analysis of Variance (ANOVA)

  • Focuses on the analysis of variance (ANOVA) methodology.

Resource:

DAY 16:

Topic: Exploratory Data Analysis (EDA)

The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.

• Examine the data distribution
• Handling missing values of the dataset(a most common issue with every dataset)
• Handling the outliers
• Removing duplicate data
• Encoding the categorical variables
• Normalizing and Scaling

Resource

Day 17:

Topic: SQL Basics Concepts

• Creating Database: Learn how to create your own database.
• Creating Tables and Adding Data: Understand how to create tables and insert data into them.
• SELECT Clause: Learn to retrieve or fetch data from a database.
• FROM Clause: Understand from which table in the database you need to select data.
• WHERE Clause: Learn to form conditions based on which data have to be queried.
• DELETE Statement: Understand how to perform deletion tasks.
• INSERT INTO: Learn about insertion tasks.
• AND and OR Operator: Know how to select data based on AND or OR conditions.
• Drop and Truncate: Learn to drop or truncate collections as per the condition.
• NOT Operator: Understand how to select data not based on a given condition.

Resources

DAY 18:

Topic: SQL Advanced Concepts

• WITH Clause: Understanding the concept of the WITH clause and using it to name a sub-query block.
• FETCH Clause: Learn to fetch the filtered data based on conditions, like fetching only the top 3 rows.
• Arithmetic Operators: Use arithmetic operators for precise data filtering.
• Wildcard Operators: Select exact data intelligently, like names starting or ending with 'T'.
• UPDATE Statement: Learn updating data entries based on conditions.
• ALTER Table: Know how to add, drop, or modify tables.
• LIKE Clause: Understand pattern-based search.
• BETWEEN and IN Operator: Learn to select data within a specified range.
• CASE Statement: Understand conditional queries.
• EXISTS: Learn to form nested queries for filtering data that exists in another query.

Resources

DAY 19:

Topic: SQL Aggregate Functions

• DISTINCT Clause: Select only distinct, non-repetitive data.
• Count Function: Learn to return the total count of filtered data.
• Sum Function: Understand how to calculate the sum of queried data.
• Average Function: Calculate the average of queried data.
• Minimum Function: Learn to find the minimum value in queried data.
• Maximum Function: Learn to find the maximum value in queried data.
• ORDER BY: Order queried data in ascending or descending order.
• GROUP BY: Group queried data by a specified column.
• ALL and ANY Clause: Understand these logical operators and their boolean results.
• TOP Clause: Learn to fetch a limited number of rows from a database.

Resources

DAY 20:

Topic: Joins in SQL

• Union Clause: Understand the union of tables.
• Intersection Clause: Learn to join tables at their intersection.
• Aliases: Assign aliases to tables for later reference.
• Cartesian Join and Self Join: Learn to join a table to itself.
• Inner, Left, Right, and Full Joins: Understand these four types of joins.
• Division Clause: Find entities interacting with all entities of a set of different types.
• Using Clause: Modify NATURAL JOIN with the USING clause for columns with the same names but different datatypes.
• Combining Values: Combine aggregate and non-aggregate values using Joins and Over clause.
• MINUS Operator: Understand how to use the MINUS operator for exclusion.
• Joining 3 or More Tables: Learn to join and query from three or more tables.

Resources

Project to practice first download the dataset here than try to practice all the SQL Queries in the readme file, after this you can see the correction here

Don't focus on the Tableau section; we will delve into it in the previous lessons.

DAY 21:

Topic: Introduction to Machine Learning

• Machine Learning Definition
• Examples and Use Cases
  • Recommendation engines (Amazon, Spotify, Netflix).
  • Speech recognition software.
  • Fraud detection services in banks.
  • Self-driving cars and driver assistance features.
• How Does Machine Learning Work?
• Machine Learning vs. Deep Learning
• Types of Machine Learning
  • Supervised Machine Learning: Trained on labeled data sets.
  • Unsupervised Machine Learning: Uses unlabeled data sets to uncover patterns.
  • Semi-supervised Machine Learning: Combines labeled and unlabeled data sets.
  • Reinforcement Learning: Uses trial and error in specific environments.
• Machine Learning Benefits and Risks

Resources

DAY 22 :

Topic: Steps in Machine Learning Project

• Define the Problem: Identify the problem you want to solve and determine if machine learning can be used to solve it.
• Collect Data: Gather and clean the data that you will use to train your model. The quality of your model will depend on the quality of your data.
• Explore the Data: Use data visualization and statistical methods to understand the structure and relationships within your data.
• Pre-process the Data: Prepare the data for modeling by normalizing, transforming, and cleaning it as necessary.
• Split the Data: Divide the data into training and test datasets to validate your model.
• Choose a Model: Select a machine learning model that is appropriate for your problem and the data you have collected.
• Train the Model: Use the training data to train the model, adjusting its parameters to fit the data as accurately as possible.
• Evaluate the Model: Use the test data to evaluate the performance of the model and determine its accuracy.
• Fine-tune the Model: Based on the results of the evaluation, fine-tune the model by adjusting its parameters and repeating the training process until the desired level of accuracy is achieved.

Resources

practical Project : This project is quite popular. You can discover all these steps. Take your time because these steps exist in every project.

DAY 23 :

Topic: Exploring Scikit-Learn

first if you don´t know which algorithm are you going to use. You shouldn't be worried about it. Scikit-learn tells you what to do: We will discover:

1. Basic Example: A simple example using Scikit-Learn for machine learning.
2. Data Loading: Guidelines on data requirements and loading techniques.
3. Model Fitting: Instructions for fitting both supervised and unsupervised learning models.
4. Prediction: Methods to make predictions using different estimators.
5. Data Preprocessing: Techniques for standardization, normalization, binarization, and encoding categorical features.
6. Model Creation: Steps to create supervised and unsupervised learning estimators.
7. Model Evaluation: Various metrics for assessing the performance of models.
8. Model Tuning: Strategies for tuning models using grid search and randomized parameter optimization.

Resource This is a PDF file that can provide you with all of the above.

DAY 24 :

Topic: Advanced Scikit-Learn Features

• Introduction to Scikit-Learn: Basic concepts and workflow.
• Data Preprocessing: Techniques for preparing data for modeling.
• Supervised Learning Models: Instructions for creating and using models like regression and classification.
• Unsupervised Learning Models: Guides on clustering and dimensionality reduction.
• Model Tuning and Evaluation: Tips on improving model performance and measuring accuracy.
• Pipeline and Model Complexity: Insights into streamlining workflows and handling complex data scenarios.

Resources This is a PDF file that can provide you with all of the above.

Resource

DAY 25 :

Topic: Feature Engineering 1 - Handling Missing Values

1. Handling Missing values
   • 1.1 Problems of Having Missing values
   • 1.2 Understanding Types of Missing Values
   • 1.3 Dealing MV Using SimpleImputer Method
   • 1.4 Dealing MV Using KNN Imputer Method
2. Handling Categorical Values
   • 2.1 One Hot Encoding
   • 2.2 Label Encoding
   • 2.3 Ordinal Encoding
   • 2.4 Multi Label Binarizer
   • 2.5 Count/Frequency Encoding
   • 2.6 Target Guided Ordinal Encoding

DAY 26:

Topic: Feature Engineering 2 - Feature Scaling

1. Feature Scaling
   • 1.1 Standardization/Standard Scaler
   • 1.2 Normalization/MinMax Scaler
   • 1.3 Max Abs Scaler
   • 1.4 Robust Scaler

DAY 27:

Topic: Feature Engineering 3 - Feature Selection

1. Why Feature Selection Matters
2. Types of Feature Selection
3. Filter Methods
   • Variance Threshold
   • SelectKBest
   • SelectPercentile
   • GenericUnivariateSelect
4. Wrapper Methods
   • RFE
   • RFECV
   • SelectFromModel
   • SequentialFeatureSelector

DAY 28:

Topic: Feature Engineering 4 - Feature Transformation and Pipelines

1. Feature Transformation
   • Understanding QQPlot and PP-Plot
   • Logarithmic transformation
   • Reciprocal transformation
   • Square root transformation
   • Exponential transformation
   • Boxcox transformation
2. Using Pipelines to automate the FE
   • What are Pipelines
   • Accessing individual steps in pipeline
   • Accessing Parameters in Pipeline
   • Performing Grid Search with Pipeline
   • Combining Transformers and Pipeline
   • Visualizing the Pipeline

DAY 29:

Topic: Understanding Linear Regression

1. Fundamentals of Linear Regression
2. Exploring the Assumptions of Linear Regression
3. Gradient Descent and Loss Function
4. Evaluation Metrics for Linear Regression
5. Applications of Linear Regression

DAY 30:

Topic: Understanding Multicollinearity, and Regularization Techniques

1. Multiple Linear Regression
2. Multicollinearity
3. Regularization Techniques
4. Ridge, Lasso and Elastic Net
5. Polynomial Regression

DAY 31:

Topic: Understanding the Logistic Regression

1. How does Logistic Regression work
2. What is a sigmoid curve
3. Assumptions of Logistic Regression
4. Cost Function of Logistic Regression

DAY 32:

Topic: Understanding Decision Trees

1. Why do we need Decision Trees
2. How does Decision Trees work
3. How do we select a root node
4. Understanding Entropy, Information Gain
5. Solving an Example on Entropy
6. Understanding Gini Impurity
7. Solving an Example on Gini Impurity
8. Decision Trees for Regression
9. Why decision trees are Greedy Approach
10. Understanding Pruning

DAY 33:

Topic: Understanding Ensemble Techniques

1. What are Ensemble Techniques
2. Understanding Bagging
3. Understanding Boosting
4. Understanding Stacking

DAY 34:

Topic: Understanding Random Forests

1. Decision Trees Aggregation
2. Bagging and Variance Reduction
3. Feature Subspace sampling
4. Handling Overfitting
5. Out of bag error

DAY 35:

Topic: Understanding Boosting Algorithms

1. Concept of Boosting
2. Understanding Ada Boost
3. Solving an Example on AdaBoost
4. Understanding Gradient Boosting
5. Solving an Example on Gradient Boosting
6. AdaBoost vs Gradient Boosting

DAY 36:

Topic: Understanding XG Boost Algorithms

1. Concept of XGBoost Algorithm
2. Boosting Mechanism
3. Feature Importance Interpretation
4. Regularization Techniques
5. Flexibility and Scalability

DAY 37:

Topic: Understanding K Nearest Neighbours

1. How does K-Nearest Neighbours work
2. How is Distance Calculated
   • Euclidean Distance
   • Hamming Distance
   • Manhattan Distance
3. Why is KNN a Lazy Learner
4. Effects of Choosing the value of K
5. Different ways to perform KNN
6. Understanding KD-Tree
7. Solving an Example of KD Tree
8. Understanding Ball Tree

DAY 38:

Topic: Understanding Support Vector Machines

1. Understanding Concept of SVC
2. What are Support Vectors
3. What is Margin
4. Hard Margin and Soft Margin
5. Kernelized SVC
6. Types of Kernels
7. Understanding SVR

DAY 39:

Topic: Understanding Naive Bayes Classifiers

1. Why do we need Naive Bayes
2. Concept of how it works
3. Mathematical Intuition of Naive Bayes
4. Solving an Example on Naive Bayes
5. Other Bayes Classifiers
   • Gaussian Naive Bayes Classifier
   • Multinomial Naive Bayes Classifier
   • Bernoulli Naive Bayes Classifier

DAY 40:

Topic: Understanding Clustering Techniques

1. How clustering is different from classification
2. Applications of Clustering
3. What are density based methods
4. What are Hierarchial based methods
5. What are partitioning methods
6. What are Grid Based methods
7. Main Requirements for Clustering Algorithms

DAY 41:

Topic: Understanding K-Means Clustering

1. Concept of K-Means Clustering
2. Math Intuition Behind K-Means
3. Cluster Building Process
4. Edge Case Scenarios of K-Means
5. Challenges and Improvements in K-Means

DAY 42:

Topic: Understanding Hierarchical Clustering

1. Concept of Hierarchical Clustering
2. Understanding Algorithm
3. Understanding Linkage Methods

DAY 43:

Topic: Understanding DB SCAN Clustering

1. Concept of DB SCAN
2. Key words in understanding DB SCAN
3. Algorithm of DB SCAN

DAY 44:

Topic: Evaluation of Clustering Models

1. Understanding External Measures
   • Rand Index
   • Jaccard Co-efficient
2. Understanding Internal Measures
   • Cohesion
   • Separation

DAY 45:

Topic: Understanding Curse of Dimensionality

1. Computational Complexity
2. Data Visualization Challenges

DAY 46:

Topic: Understanding Principal Component Analysis

1. Idea Behind PCA
2. What are Principal Components
3. Eigen Decomposition Approach
4. Singular Value Decomposition Approach
5. Why do we maximize Variance
6. What is Explained Variance Ratio
7. How to select optimal number of Principal Components
8. Understanding Scree plot
9. Issues with PCA
10. Understanding Kernel PCA

DAY 47:

Topic: Supervised Algorithms Revision

• Regression Algorithms
  1. Linear Regression
  2. Polynomial Regression
• Classification Algorithms
  1. K-Nearest Neighbours
  2. Logistic Regression
• Both Classification and Regression
  1. Decision Trees
  2. Random Forest
  3. Gradient Boosting
  4. Ada Boost
  5. Ridge Regression
  6. Lasso Regression

DAY 48:

Topic: UnSupervised Algorithms Revision

• Clustering Algorithms
  1. K-Means
  2. DBSCAN
  3. HDBSCAN
  4. Hierarchical
• Dimensionality Reduction Techniques
  1. PCA
  2. t-SNE
  3. ICA
• Association Rules
  1. Apriori
  2. FP-growth
  3. FP-Max

DAY 49:

Topic: Big Mart Sales Prediction Project Understanding

1. Understanding the Data

DAY 50:

Topic: EDA for Big Mart Sales

1. Dealing with Null Values
2. Data Visualization of the Numeric Columns
3. Feature Engineering of the Numeric Columns

DAY 51:

Topic: Data Visualization

1. Data Visualization of the Categorical Columns
2. Feature Engineering of the Categorical Columns

DAY 52:

Topic: Model Building and Evaluation

• Model Selection: Choosing the right model for the problem (classification, regression, etc.).
• Training and Testing: Splitting data into training and testing sets to evaluate model performance.
• Evaluation Metrics: Using metrics like accuracy, precision, recall, and MSE for performance assessment.
• Cross-Validation: Implementing cross-validation techniques for more reliable model evaluation.
• Model Interpretability: Understanding and explaining model decisions.

DAY 53

Topic: Hyperparameter Tuning the Models

• Hyperparameter Basics: Understanding what hyperparameters are in machine learning models.
• Tuning Techniques: Introducing Grid Search, Random Search, and Bayesian Optimization.
• Practical Implementation: Applying hyperparameter tuning on a sample model.
• Performance Impact: Assessing how hyperparameters influence model outcomes.
• Best Practices: Discussing balance in model complexity and overfitting.

DAY 54: History of Deep Learning

Topics:

1. Early Developments: Tracing the origins and initial concepts of neural networks.
2. Key Milestones: Highlighting major breakthroughs and influential models in deep learning.
3. Deep Learning Resurgence: Understanding the factors contributing to the modern rise of deep learning.
4. Influential Models: Overview of landmark models in deep learning history.
5. Future Trends: Discussing current trends and potential future developments in deep learning.

DAY 55: Deep Learning Frameworks

Topics:

1. Introduction to TensorFlow
2. Introduction to PyTorch
3. Comparison of Frameworks
4. Setting Up a Simple Neural Network in Both Frameworks

DAY 56: Introduction to Neural Networks

Topics:

1. Neural Network Structure: Exploring the basic architecture including neurons and layers.
2. Forward Propagation: Understanding how data is processed in a neural network.
3. Backpropagation and Training: Learning the mechanism of training neural networks.
4. Activation Functions: Introduction to different activation functions and their purposes.
5. Simple Implementation: Hands-on example of creating a basic neural network.

DAY 57: Convolutional Neural Networks (CNNs)

Topics:

1. Understanding the Architecture of CNNs
2. Applications in Image Recognition
3. Implementing a Basic CNN

DAY 58: Recurrent Neural Networks (RNNs) and LSTM

Topics:

1. Architecture of RNNs
2. Long Short-Term Memory (LSTM) Networks
3. Applications in Time Series and Text

DAY 59: Transfer Learning and Fine-Tuning

Topics:

1. Concepts of Transfer Learning
2. Pre-trained Models
3. Fine-Tuning Techniques

DAY 60: Generative Adversarial Networks (GANs)

Topics:

1. Introduction to GANs
2. Understanding Generator and Discriminator
3. Simple GAN Implementation

DAY 61: Reinforcement Learning Basics

Topics:

1. Understanding the Reinforcement Learning Framework
2. Markov Decision Processes
3. Basic Algorithms in Reinforcement Learning

DAY 62: Advanced Topics in Reinforcement Learning

Topics:

1. Deep Q-Networks (DQN)
2. Policy Gradient Methods
3. Real-world Applications

DAY 63: Model Deployment

Topics:

1. Introduction to Model Deployment
2. Flask for Python
3. Docker Basics

DAY 64: Ethics in AI and Data Science

Topics:

1. Understanding Bias in Data
2. Ethical Considerations in AI
3. Privacy and Data Security

DAY 65: Text Mining and Natural Language Processing (NLP) Basics

Topics:

1. Text Preprocessing Techniques
2. Introduction to NLTK and Spacy
3. Basic Text Analysis

DAY 66: Advanced NLP Techniques

Topics:

1. Sentiment Analysis
2. Topic Modeling
3. Named Entity Recognition

DAY 67: Introduction to Computer Vision

Topics:

1. Basic Image Processing Techniques
2. OpenCV Basics
3. Simple Applications in Image Analysis

DAY 68: Advanced Computer Vision Techniques

Topics:

1. Object Detection
2. Face Recognition
3. Optical Character Recognition (OCR)

DAY 69: Cloud Services for Machine Learning

Topics:

1. Introduction to AWS Sagemaker
2. Introduction to Google Cloud ML Engine
3. Deploying Models on Cloud Platforms

DAY 70: Big Data Technologies

Topics:

1. Introduction to Big Data Ecosystem
2. Basics of Apache Hadoop and Spark
3. Big Data Processing and Analysis

DAY 71: Introduction to Tableau

Topics:

1. Getting Started with Tableau
2. Basic Data Visualization Concepts
3. Creating Dashboards and Stories in Tableau

DAY 72: Tableau Intermediate

Topics:

1. Advanced Data Visualization Techniques
2. Working with Different Data Sources in Tableau
3. Interactive Dashboards and Data Exploration

DAY 73: Tableau Advanced

Topics:

1. Advanced Calculations in Tableau (Table Calculations, LOD Expressions)
2. Working with Geospatial Data in Tableau
3. Performance Optimization in Tableau

DAY 74: Capstone Project with Tableau

Topics:

1. Applying Tableau Skills in a Real-World Scenario
2. Creating an End-to-End Data Visualization Project
3. Storytelling with Data using Tableau

DAY 75: Introduction to Power BI

Topics:

1. Getting Started with Power BI
2. Basic Data Modeling and Visualization
3. Creating Reports and Dashboards in Power BI

DAY 76: Power BI Intermediate

Topics:

1. DAX Basics: Data Analysis Expressions
2. Advanced Data Modeling in Power BI
3. Interactive Reports and Dashboard Design

DAY 77: Power BI Advanced

Topics:

1. Advanced DAX for Complex Calculations
2. Working with Power BI Service (Cloud)
3. Data Refresh, Security, and Administration in Power BI

DAY 78: Capstone Project with Power BI

Topics:

1. Applying Power BI Skills in a Business Context
2. End-to-End Business Intelligence Project
3. Sharing Insights and Dashboards with Stakeholders

DAY 79: Integration of Tableau and Power BI with Machine Learning

Topics:

1. Leveraging Machine Learning Models in Tableau and Power BI
2. Visualizing Machine Learning Outputs
3. Advanced Analytics with Tableau and Power BI

DAY 80: Final Capstone Project Presentation

Topics:

1. Finalizing the Capstone Project
2. Presenting the Project to Peers or Stakeholders
3. Receiving Feedback and Reflecting on the Learning Journey