Data Science Diploma

Diploma Info

Date: Oct-07-2024

Length: 3 Months

City: London

Fees: 11,700

Type: In Classroom

Available Dates

  • Oct-07-2024

    London

Dates in Other Venues

Diploma Details

Diploma Outline

Months of diploma

Month 1

Week 1

Day 1: Introduction to Data Science

  • Definition and objectives of data science
  • Role and skills of data scientists
  • Importance of data science in various industries

Day 2: Basics of Statistics for Data Science

  • Descriptive statistics
  • Inferential statistics
  • Probability distributions

Day 3: Introduction to Programming for Data Science

  • Introduction to Python for data science
  • Understanding data structures in Python
  • Basics of R for data science

Week 2

Day 1: Data Manipulation and Analysis

  • Data cleaning and preprocessing
  • Introduction to pandas for data manipulation
  • Data analysis using pandas

Day 2: Data Visualization

  • Principles of data visualization
  • Data visualization using Matplotlib
  • Advanced data visualization using Seaborn

Day 3: Exploratory Data Analysis

  • Understanding Exploratory Data Analysis (EDA)
  • Implementing EDA using Python
  • EDA case studies

Week 3

Day 1: Introduction to Databases

  • Understanding databases
  • SQL basics for data extraction
  • Advanced SQL queries

Day 2: Basics of Machine Learning

  • Introduction to Machine Learning
  • Supervised Learning algorithms
  • Unsupervised Learning algorithms

Day 3: Feature Engineering and Model Evaluation

  • Understanding feature engineering
  • Cross-validation techniques
  • Metrics for model evaluation

Week 4

Day 1: Supervised Learning in Depth

  • Linear regression
  • Logistic regression
  • Decision trees

Day 2: Unsupervised Learning in Depth

  • K-means clustering
  • Hierarchical clustering
  • Principal Component Analysis

Day 3: Introduction to Deep Learning

  • Understanding neural networks
  • Basics of deep learning
  • Introduction to TensorFlow and Keras

Month 2

Week 1

Day 1: Advanced Machine Learning

  • Introduction to ensemble methods
  • Random forests
  • Gradient boosting methods

Day 2: Natural Language Processing

  • Introduction to Natural Language Processing (NLP)
  • Text preprocessing techniques
  • Implementing NLP using NLTK library

Day 3: Recommender Systems

  • Introduction to recommender systems
  • Collaborative filtering
  • Content-based filtering

Week 2

Day 1: Time Series Analysis

  • Understanding time series data
  • Time series forecasting methods
  • ARIMA models

Day 2: Introduction to Big Data

  • Understanding Big Data
  • Big Data tools and technologies
  • Introduction to Hadoop and Spark

Day 3: Data Science Project Lifecycle

  • Understanding the CRISP-DM framework
  • Data understanding and preparation
  • Modelling and Evaluation

Week 3

Day 1: Data Ethics and Privacy

  • Understanding data ethics
  • Importance of data privacy
  • Legal and ethical considerations in data science

Day 2: Advanced Deep Learning

  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)

Day 3: Reinforcement Learning

  • Introduction to reinforcement learning
  • Understanding Q-learning
  • Deep Q-learning

Week 4

Day 1: Deploying Data Science Models

  • Understanding model deployment
  • Basics of Flask for model deployment
  • Docker for data science

Day 2: Cloud Computing for Data Science

  • Introduction to cloud computing
  • Basics of AWS for data science
  • Introduction to Google Cloud and Azure

Day 3: Real-time Analytics

  • Understanding real-time analytics
  • Tools for real-time analytics
  • Case studies in real-time analytics

Month 3

Week 1

Day 1: Data Science in Business

  • Understanding the role of data science in business
  • Data-driven decision making
  • Building a data-driven culture in organizations

Day 2: Data Strategy

  • Understanding data strategy
  • Creating a data strategy for an organization
  • Case studies of successful data strategies

Day 3: Future Trends in Data Science

  • AI and Machine Learning trends
  • Importance of AutoML
  • The role of AI ethics in the future of data science

Week 2

Day 1: Industry Applications of Data Science

  • Data Science in healthcare
  • Data Science in finance
  • Data Science in marketing

Day 2: Advanced Topics in AI

  • Introduction to AI and robotics
  • AI in natural language understanding
  • AI in image and video processing

Day 3: Case Studies in Data Science

  • Case study: Netflix recommendation engine
  • Case study: Predictive policing
  • Case study: Real-time analytics in sports

Week 3

Day 1: Communication and Storytelling in Data Science

  • Importance of communication in data science
  • Storytelling with data
  • Creating compelling data presentations

Day 2: Advanced Analytics Techniques

  • Introduction to Bayesian Inference
  • Markov chains and Monte Carlo methods
  • Optimisation Techniques

Day 3: Special Topics in Data Science

  • Geospatial Data Analysis
  • Social Network Analysis
  • Anomaly Detection

Week 4

Day 1: Multimodal Data Fusion

  • Introduction to Multimodal Data
  • Techniques for Data Fusion
  • Applications of Multimodal Data Analysis

Day 2: Data Science in Social Sciences

  • Data Science in Sociology
  • Data Science in Political Science
  • Data Science in Psychology

Day 3: Quantum Computing and Data Science

  • Introduction to Quantum Computing
  • Quantum Machine Learning
  • Impact of Quantum Computing on Data Science

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