Data Analytics & AI | SQL Interviews | Power BI Resources

Day 26: Reinforcement Learning
- Concept: Learning through interaction.
- Implementation: Q-learning.
- Evaluation: Reward function, policy.

Day 27: Bayesian Networks
- Concept: Probabilistic graphical models.
- Implementation: Conditional dependencies.
- Evaluation: Inference, learning.

Day 28: Hidden Markov Models (HMM)
- Concept: Time series analysis.
- Implementation: Transition probabilities.
- Evaluation: Viterbi algorithm.

Day 29: Feature Selection Techniques
- Concept: Improving model performance.
- Implementation: Filter, wrapper methods.
- Evaluation: Feature importance.

Day 30: Hyperparameter Optimization
- Concept: Model tuning.
- Implementation: Grid search, random search.
- Evaluation: Cross-validation.

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Let's start with the topics we gonna cover in this 30 Days of Data Science Series,

We will primarily focus on learning Data Science and Machine Learning Algorithms

Day 1: Linear Regression
- Concept: Predict continuous values.
- Implementation: Ordinary Least Squares.
- Evaluation: R-squared, RMSE.

Day 2: Logistic Regression
- Concept: Binary classification.
- Implementation: Sigmoid function.
- Evaluation: Confusion matrix, ROC-AUC.

Day 3: Decision Trees
- Concept: Tree-based model for classification/regression.
- Implementation: Recursive splitting.
- Evaluation: Accuracy, Gini impurity.

Day 4: Random Forest
- Concept: Ensemble of decision trees.
- Implementation: Bagging.
- Evaluation: Out-of-bag error, feature importance.

Day 5: Gradient Boosting
- Concept: Sequential ensemble method.
- Implementation: Boosting.
- Evaluation: Learning rate, number of estimators.

Day 6: Support Vector Machines (SVM)
- Concept: Classification using hyperplanes.
- Implementation: Kernel trick.
- Evaluation: Margin maximization, support vectors.

Day 7: k-Nearest Neighbors (k-NN)
- Concept: Instance-based learning.
- Implementation: Distance metrics.
- Evaluation: k-value tuning, distance functions.

Day 8: Naive Bayes
- Concept: Probabilistic classifier.
- Implementation: Bayes' theorem.
- Evaluation: Prior probabilities, likelihood.

Day 9: k-Means Clustering
- Concept: Partitioning data into k clusters.
- Implementation: Centroid initialization.
- Evaluation: Inertia, silhouette score.

Day 10: Hierarchical Clustering
- Concept: Nested clusters.
- Implementation: Agglomerative method.
- Evaluation: Dendrograms, linkage methods.

Day 11: Principal Component Analysis (PCA)
- Concept: Dimensionality reduction.
- Implementation: Eigenvectors, eigenvalues.
- Evaluation: Explained variance.

Day 12: Association Rule Learning
- Concept: Discover relationships between variables.
- Implementation: Apriori algorithm.
- Evaluation: Support, confidence, lift.

Day 13: DBSCAN (Density-Based Spatial Clustering of Applications with Noise)
- Concept: Density-based clustering.
- Implementation: Epsilon, min samples.
- Evaluation: Core points, noise points.

Day 14: Linear Discriminant Analysis (LDA)
- Concept: Linear combination for classification.
- Implementation: Fisher's criterion.
- Evaluation: Class separability.

Day 15: XGBoost
- Concept: Extreme Gradient Boosting.
- Implementation: Tree boosting.
- Evaluation: Regularization, parallel processing.

Day 16: LightGBM
- Concept: Gradient boosting framework.
- Implementation: Leaf-wise growth.
- Evaluation: Speed, accuracy.

Day 17: CatBoost
- Concept: Gradient boosting with categorical features.
- Implementation: Ordered boosting.
- Evaluation: Handling of categorical data.

Day 18: Neural Networks
- Concept: Layers of neurons for learning.
- Implementation: Backpropagation.
- Evaluation: Activation functions, epochs.

Day 19: Convolutional Neural Networks (CNNs)
- Concept: Image processing.
- Implementation: Convolutions, pooling.
- Evaluation: Feature maps, filters.

Day 20: Recurrent Neural Networks (RNNs)
- Concept: Sequential data processing.
- Implementation: Hidden states.
- Evaluation: Long-term dependencies.

Day 21: Long Short-Term Memory (LSTM)
- Concept: Improved RNN.
- Implementation: Memory cells.
- Evaluation: Forget gates, output gates.

Day 22: Gated Recurrent Units (GRU)
- Concept: Simplified LSTM.
- Implementation: Update gate.
- Evaluation: Performance, complexity.

Day 23: Autoencoders
- Concept: Data compression.
- Implementation: Encoder, decoder.
- Evaluation: Reconstruction error.

Day 24: Generative Adversarial Networks (GANs)
- Concept: Generative models.
- Implementation: Generator, discriminator.
- Evaluation: Adversarial loss.

Day 25: Transfer Learning
- Concept: Pre-trained models.
- Implementation: Fine-tuning.
- Evaluation: Domain adaptation.

Starting your journey as a data analyst is an amazing start for your career. As you progress, you might find new areas that pique your interest:

• Data Science: If you enjoy diving deep into statistics, predictive modeling, and machine learning, this could be your next challenge.

• Data Engineering: If building and optimizing data pipelines excites you, this might be the path for you.

• Business Analysis: If you're passionate about translating data into strategic business insights, consider transitioning to a business analyst role.

But remember, even if you stick with data analysis, there's always room for growth, especially with the evolving landscape of AI.

No matter where your path leads, the key is to start now.

CHATGPT Ultimate Guide

Complete Syllabus for Data Analytics interview:

SQL:
1. Basic   
- SELECT statements with WHERE, ORDER BY, GROUP BY, HAVING   
- Basic JOINS (INNER, LEFT, RIGHT, FULL)   
- Creating and using simple databases and tables

2. Intermediate   
- Aggregate functions (COUNT, SUM, AVG, MAX, MIN)   
- Subqueries and nested queries
- Common Table Expressions (WITH clause)   
- CASE statements for conditional logic in queries
3. Advanced   
- Advanced JOIN techniques (self-join, non-equi join)   
- Window functions (OVER, PARTITION BY, ROW_NUMBER, RANK, DENSE_RANK, lead, lag)   
- optimization with indexing   
- Data manipulation (INSERT, UPDATE, DELETE)

Python:
1. Basic   
- Syntax, variables, data types (integers, floats, strings, booleans)   
- Control structures (if-else, for and while loops)   
- Basic data structures (lists, dictionaries, sets, tuples)   
- Functions, lambda functions, error handling (try-except)   
- Modules and packages

2. Pandas & Numpy   
- Creating and manipulating DataFrames and Series   
- Indexing, selecting, and filtering data   
- Handling missing data (fillna, dropna)   
- Data aggregation with groupby, summarizing data   
- Merging, joining, and concatenating datasets

3. Basic Visualization   
- Basic plotting with Matplotlib (line plots, bar plots, histograms)   
- Visualization with Seaborn (scatter plots, box plots, pair plots)   
- Customizing plots (sizes, labels, legends, color palettes)   
- Introduction to interactive visualizations (e.g., Plotly)

Excel:
1. Basic   
- Cell operations, basic formulas (SUMIFS, COUNTIFS, AVERAGEIFS, IF, AND, OR, NOT & Nested Functions etc.)   
- Introduction to charts and basic data visualization   
- Data sorting and filtering   
- Conditional formatting

2. Intermediate   
- Advanced formulas (V/XLOOKUP, INDEX-MATCH, nested IF)   
- PivotTables and PivotCharts for summarizing data   
- Data validation tools   
- What-if analysis tools (Data Tables, Goal Seek)

3. Advanced   
- Array formulas and advanced functions   
- Data Model & Power Pivot
- Advanced Filter
- Slicers and Timelines in Pivot Tables   
- Dynamic charts and interactive dashboards

Power BI:
1. Data Modeling   
- Importing data from various sources   
- Creating and managing relationships between different datasets   
- Data modeling basics (star schema, snowflake schema)

2. Data Transformation   
- Using Power Query for data cleaning and transformation   
- Advanced data shaping techniques   
- Calculated columns and measures using DAX

3. Data Visualization and Reporting   - Creating interactive reports and dashboards   
- Visualizations (bar, line, pie charts, maps)   
- Publishing and sharing reports, scheduling data refreshes

Statistics Fundamentals: Mean, Median, Mode, Standard Deviation, Variance, Probability Distributions, Hypothesis Testing, P-values, Confidence Intervals, Correlation, Simple Linear Regression, Normal Distribution, Binomial Distribution, Poisson Distribution.

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Artificial Intelligence for Robotics
Francis X. Govers, 2018

Any person learning deep learning or artificial intelligence in particular, know that there are ultimately two paths that they can go:

1. Computer vision
2. Natural language processing.

I outlined a roadmap for computer vision I believe many beginners will find helpful.

Artificial Intelligence

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Data Analyst Interview Questions

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Scientific Visualisation
Nicolai P. Rougier, 2021

Data Visualization
Kieran Healy, 2019

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