Certificate in Data Analysis Deep Learning

Description

Duration: 12 Weeks (Full-Time) | 24 Weeks (Part-Time/Evenings)

Mode of Delivery: Online (Live Virtual Classes) or On-Campus

Who is this for?

• Aspiring Data Analysts and Data Scientists

• Software Engineers looking to transition into AI/ML roles

• Business & Financial Analysts seeking to leverage predictive modeling

• Professionals from any field who want to add data-driven decision-making to their skillset

• Students and recent graduates aiming to build a portfolio for a tech career

Prerequisites:

• High school-level mathematics (familiarity with algebra is helpful).

• Basic programming knowledge is beneficial but not mandatory. An introductory Python module is included.

• A strong willingness to learn and solve complex problems.

Program Learning Objectives

Upon successful completion of this certificate, you will be able to:

• Wrangle and clean messy real-world datasets using Python (Pandas, NumPy).

• Analyze data statistically and create compelling visualizations (Matplotlib, Seaborn).

• Design, query, and manage relational databases using SQL.

• Understand the fundamental concepts and mathematics behind Machine Learning and Neural Networks.

• Build, train, and evaluate various deep learning models including CNNs and RNNs.

• Deploy a trained model into a simple web application for inference.

• Develop a professional portfolio of projects to showcase to employers.

Detailed Curriculum

Module 1: Foundations of Data Analysis with Python (Weeks 1-3)

• Introduction to Python for Data Science: Syntax, data structures, functions, and libraries.

• Data Wrangling with Pandas: DataFrames, Series, reading data, handling missing values, filtering, grouping, and merging datasets.

• Numerical Computing with NumPy: Arrays, array operations, and broadcasting.

• Data Visualization: Creating informative static and interactive plots with Matplotlib and Seaborn.

• Project 1: Exploratory Data Analysis (EDA) on a real-world dataset (e.g., sales records, movie ratings).

Module 2: Databases & SQL for Data Analysts (Week 4)

• Relational Database Fundamentals: Schemas, tables, keys, and relationships.

• SQL Queries: SELECT, WHERE, JOIN (INNER, LEFT, RIGHT), GROUP BY, HAVING, and ORDER BY.

• Advanced SQL: Subqueries, Common Table Expressions (CTEs), and window functions.

• Connecting Python to SQL Databases.

Module 3: Mathematics for Machine Learning (Week 5)

• Linear Algebra: Vectors, matrices, dot products, and matrix multiplication.

• Calculus: Introduction to derivatives and gradients (conceptual focus for understanding gradient descent).

• Statistics & Probability: Descriptive stats, probability distributions, Bayes’ Theorem.

Module 4: Introduction to Machine Learning (Weeks 6-7)

• ML Fundamentals: Supervised vs. Unsupervised Learning, training/testing split, bias-variance tradeoff.

• Key Algorithms:

  • Linear & Logistic Regression

  • Decision Trees and Random Forests

  • k-Nearest Neighbors (k-NN)

  • Clustering with k-Means

• Model Evaluation: Accuracy, Precision, Recall, F1-Score, ROC curves, cross-validation.

• Project 2: Building a predictive ML model to solve a classification or regression problem.

Module 5: Deep Learning Fundamentals (Weeks 8-9)

• Introduction to Neural Networks: Perceptrons, activation functions (Sigmoid, ReLU), layers.

• Training Neural Networks: Loss functions, gradient descent, backpropagation.

• Frameworks: Introduction to TensorFlow and Keras.

• Building Your First Neural Network for image or numerical data.

• Optimization & Regularization: Overfitting, dropout, batch normalization.

Module 6: Advanced Deep Learning Architectures (Weeks 10-11)

• Convolutional Neural Networks (CNNs): Architecture, convolutions, pooling layers for image recognition.

• Transfer Learning: Using pre-trained models (e.g., VGG16, ResNet) for custom tasks.

• Recurrent Neural Networks (RNNs) & LSTMs: Architecture for sequential data (time series, text).

• Project 3: Capstone Project – Build a deep learning model (e.g., image classifier, sentiment analysis tool, time series forecaster).

Module 7: Deployment & Capstone Project (Week 12)

• Model Deployment: Introduction to deploying a model as a REST API using Flask.

• Building a Data Portfolio: Best practices for showcasing projects on GitHub.

• Career Session: Resume writing for data roles, interview preparation.

• Final Capstone Presentation: Students present their Project 3 to instructors and peers.

Tools & Technologies Covered

• Programming Language: Python

• Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn

• Deep Learning Frameworks: TensorFlow, Keras, PyTorch (Introduction)

• Database: SQL (MySQL or PostgreSQL)

• Development Environment: Jupyter Notebooks, Google Colab, VS Code

• Version Control: Git & GitHub

• Deployment: Flask (Introduction)

Assessment & Certification

• Weekly Coding Assignments (30%)

• Module Projects (Projects 1 & 2 – 30%)

• Final Capstone Project (Project 3 – 40%)

• A certificate of completion will be awarded to students who maintain a passing grade (e.g., 70% or higher).

Instructors

Our instructors are industry veterans with years of experience working as Data Scientists and Machine Learning Engineers at leading tech companies. They are not just teachers but mentors dedicated to your success.

Next Steps & How to Apply

Ready to start your journey?

1. Visit our website [YourWebsiteHere.com]

2. Fill out the application form.

3. Schedule a brief admissions call with an advisor.

4. Secure your seat and begin pre-course preparatory work!