MemoLearning Data Science Track

Master the art of data—from fundamentals to real-world machine learning pipelines.

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Introduction to Data Science

Overview of DS workflow, tools, and mindset.

Python for Data Science

Learn NumPy, Pandas, and essential programming.

Data Wrangling

Clean, transform, and prepare messy datasets.

Data Visualization

Charts and visual insights using Matplotlib and Seaborn.

Descriptive Statistics

Summarizing data and identifying patterns.

SQL for Data Analysis

Query, join, and extract insights from databases.

Exploratory Data Analysis

Discover trends and insights through visualization and summaries.

Inferential Statistics

Hypothesis testing and confidence intervals.

Linear Regression

Understand trends and relationships in data.

Classification Algorithms

Use decision trees, logistic regression, and more.

Clustering Techniques

Find natural groupings with k-means and DBSCAN.

Dimensionality Reduction

Use PCA and t-SNE to reduce features.

Model Evaluation & Validation

Test accuracy and avoid overfitting with cross-validation.

Machine Learning Pipelines

Scikit-learn pipelines to automate model building.

Deep Learning Introduction

Build neural networks using TensorFlow or PyTorch.

Time Series Analysis

Analyze trends and forecast with ARIMA and others.

Big Data Tools

Use Spark and Hadoop to process large datasets.

Natural Language Processing (NLP)

Work with text data and embeddings.