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Platinum Advanced Data Science Job-Ready Program

1. Introduction to Programming and Python

What is programming?
History of Python.
Setting up the development environment.
IDLE.
Jupyter Notebook.
VS Code.
Writing and running your first Python program.

Basic Syntax

Comments.
Indentation.
Printing to the console.

Variables and Data Types

Numbers (integers, floats).
Strings.
Booleans.

Basic Operators

Numbers (integers, floats).
Strings.
Booleans.

Control Flow

Conditional statements (if, elif, else).
Loops (for loops, while loops).
Break, continue, and pass statements.

Functions

Defining and calling functions: Function arguments and return values.
Scope and lifetime of variables in Lambda functions.

Data Structures: Lists

Creating and accessing lists.
List operations (indexing, slicing, adding, removing elements).
List methods.
append.
extend.
insert.
remove.
pop.
clear.
index.
count.
sort.
Reverse.

Data Structures: Tuples and Sets

Creating and accessing tuples.
Tuple methods.
Creating and accessing sets.
Set operations.
union.
intersection.
Difference.

Data Structures: Dictionaries

Creating, accessing, and modifying dictionaries: Dictionary methods.
keys.
values.
items.
get.
pop.
update.

Working with Strings

String operations.
concatenation.
slicing.
formatting.
String methods.
find.
replace.
split.
join.
lower.
upper.
Strip.

File Handling

Reading from files.
Writing to files.
Working with CSV files using the csv module.

Modules and Packages

Importing modules.
Standard library overview.
Math.
Datetime.
Random.
Installing and using third-party packages (pip).

Error Handling

Understanding exceptions.
Try, except, else, finally blocks.
Raising exceptions.

Object-Oriented Programming (OOP)

Classes and objects.
Attributes and methods.
Inheritance.
Polymorphism.

Comprehensions

List comprehensions.
Dictionary comprehensions.
Set comprehensions.

Decorators and Generators

Understanding and using decorators.
Creating and using generators.

Regular Expressions

Introduction to regular expressions.
Using the re module for pattern matching.

Advanced Data Handling with Pandas

Introduction to Pandas.
DataFrames and Series.
Reading from and writing to different file formats.
CSV.
Excel.
JSON.
Data cleaning and manipulation.

Data Visualization

Introduction to Matplotlib and Seaborn.
Plotting graphs and charts.
Customizing plots.

Web Scraping

Introduction to web scraping.
Using BeautifulSoup and requests.
Handling web scraping challenges.
Pagination.
Dynamic content.

APIs and Web Services

Understanding APIs.
Making HTTP requests using requests.
Parsing JSON data.

Working with Databases

Introduction to SQL and databases.
Using SQLite with Python.
Performing a CRUD operation.

Concurrency and Parallelism

Understanding concurrency vs. parallelism.
Using the threading and multiprocessing modules.

Testing and Debugging

Writing unit tests with unittest and pytest.
Debugging techniques and tools.

Advanced OOP Concepts

Abstract classes and interfaces.
Design patterns.
Metaclasses.

Advanced Python Libraries

Introduction to NumPy for numerical computing.
SciPy for scientific computing.
Exploring additional libraries as per interest.
TensorFlow for machine learning.

2. Statistics

2a. Introduction to Statistics

Descriptive Statistics.
Measures of central tendency: mean, median, mode.
Measures of dispersion: range, variance, standard deviation, interquartile range.
Data Visualization.
Histograms, bar charts, and pie charts.
Box plots, scatter plots.
Probability Basics.
Probability theory and rules.
Conditional probability and Bayes’ theorem.
Probability distributions: discrete and continuous.

2b. Probability Distributions

Discrete Distributions.
Bernoulli.
Binomial.
Poisson distributions.
Continuous Distributions.
Uniform and Normal (Gaussian).
Exponential distributions.
Central Limit Theorem.
Multivariate Distributions.
Multinomial distribution.
Multivariate Normal distribution.

2c. Statistical Inference

Sampling and Sampling Distributions.
Point Estimation and Properties of Estimators.
Bias.
Variance.
Mean Squared Error (MSE).
Interval Estimation.
Confidence intervals for means and proportions.
Hypothesis Testing.
Null and alternative hypotheses.
Type I and Type II errors.
p-values and significance levels.
t-tests.
chi-square tests.
ANOVA.

2d. Bayesian Statistics

Bayesian vs Frequentist methods.
Prior, likelihood, and posterior distributions.
Bayesian inference and applications.

3. Machine Learning

3a. Introduction to Machine Learning

Overview and history of machine learning.
Types of machine learning: supervised, unsupervised, reinforcement learning.
Key terminology.
Features.
Labels.
Training set.
Test set.
Validation set.

3b. Data Preprocessing

Data cleaning.
Handling missing values.
Outliers.
Feature scaling.
Normalization.
Standardization.
Encoding categorical variables.
One-hot encoding.
Label encoding.
Feature engineering and selection techniques.

3c. Supervised Learning

Linear Models.
Linear regression.
Logistic regression.
Decision Trees.
Construction and interpretation of decision trees.
Pruning techniques to avoid overfitting.
Ensemble Methods.
Bagging (Bootstrap Aggregating).
Random Forests.
Boosting (AdaBoost, Gradient Boosting).
Support Vector Machines (SVM).
Concept of hyperplane and support vectors.
Kernel tricks: linear, polynomial, radial basis function (RBF), K-Nearest Neighbors (KNN).
Distance metrics: Euclidean, Manhattan.
Choosing the value of K.
Gradient Boosting Machines (GBMs).
XGBoost.
LightGBM.
CatBoost.
Advanced SVM techniques.
Advanced kernel techniques.
Support Vector Regression (SVR).

3d. Unsupervised Learning

Clustering.
K-means clustering.
Hierarchical clustering: agglomerative and divisive.
DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
Dimensionality Reduction.
Principal Component Analysis (PCA).
t-Distributed Stochastic Neighbor Embedding (t-SNE).
Linear Discriminant Analysis (LDA).
Association Rule Learning.
Apriori algorithm.

3e. Model Evaluation and Validation

Train-test split.
Cross-validation.
k-fold.
Stratified k-fold.
Evaluation metrics for regression.
Mean Squared Error (MSE).
Root Mean Squared Error (RMSE).
Mean Absolute Error (MAE).
R².
Evaluation metrics for classification.
Accuracy.
Precision.
Recall.
F1 score.
ROC-AUC.

3f. Introduction to Neural Networks

Perceptron and multi-layer perceptron.
Activation functions.
Sigmoid.
Tanh.
ReLU.
Backpropagation and gradient descent.

3g. Introduction to Time Series Analysis

Definition and examples of time series data.
Components of time series.
Trend.
Seasonality.
Cyclic patterns.
Irregular components.
Time series vs. cross-sectional data.

3h. Descriptive Analysis

Plotting time series data.
Moving averages and smoothing techniques.
Seasonal decomposition of time series.
STL (Seasonal and Trend decomposition using Loess).

3i. Stationarity and Differencing

Definition of stationarity.
Augmented Dickey-Fuller (ADF) test for stationarity.
Differencing to achieve stationarity.
Seasonality adjustment.

3j. Autocorrelation and Partial Autocorrelation

Autocorrelation Function (ACF).
Partial Autocorrelation Function (PACF).
Identifying patterns using ACF and PACF plots.

3k. Time Series Models

Autoregressive (AR) models.
AR(p) process and Yule-Walker equations.
Moving Average (MA) models.
MA(q) process and invertibility.
Autoregressive Moving Average (ARMA) models.
ARMA(p,q) process and parameter estimation.
Autoregressive Integrated Moving Average (ARIMA) models.
ARIMA(p,d,q) process and model selection.
Seasonal ARIMA (SARIMA) models.

3l. Model Evaluation and Validation

Splitting time series data.
Training and testing sets.
Walk-forward validation.
Forecasting accuracy metrics.
Mean Absolute Error (MAE).
Mean Squared Error (MSE).
Root Mean Squared Error (RMSE).
Mean Absolute Percentage Error (MAPE).

4. Advanced Machine Learning Concepts

4a. Deep Learning

Convolutional Neural Networks (CNNs).
Convolution and pooling layers.
Famous architectures: LeNet, AlexNet, VGG, ResNet.
Recurrent Neural Networks (RNNs).
Vanishing gradient problem.
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU).

4b. Natural Language Processing (NLP)

Text Preprocessing.
Tokenization.
Stemming.
Lemmatization.
Stopwords removal.
Word Embeddings.
Word2Vec.
GloVe.
Sequence Models.
RNNs.
LSTMs.
GRUs.
Transformers.
Attention mechanism.
BERT.
GPT models.

4c. Model Deployment and Productionization

Model serving and APIs
Containerization with Docker
Model monitoring and management
Scaling machine learning models in production

5. Foundations of Generative AI (No shortcuts here)

Topics:

What is Generative AI vs Predictive AI
Types:
- LLMs
- Diffusion Models
- GANs
Tokens, embeddings, transformers
How LLMs actually work
Tools:
- Python
- Jupyter Notebook

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