Data Science Diploma training course in London

Diploma Details

Welcome to the Diploma in Data Science, a three-month comprehensive program that aims to equip you with the fundamental and advanced skills needed to become a proficient data scientist. This course combines in-depth academic instruction with practical applications, covering topics like data analysis, machine learning, artificial intelligence (AI), big data, and data visualization. Our curriculum is designed and delivered by experienced professionals to provide a strong foundation in data science along with insights into the latest trends and tools in the field.

Upon completion of this course, attendees will be able to:

Understand and apply fundamental and advanced data science concepts.
Manipulate and analyze complex datasets to extract insights.
Develop predictive models using machine learning algorithms.
Apply principles of artificial intelligence in problem-solving.
Work with big data tools and frameworks.
Design and interpret data visualizations to communicate findings.
Understand the ethical considerations in data science.
Use coding languages such as Python and R for data science tasks.

This course is perfect for:

Professionals working in IT, statistics, computer science, and related fields who want to transition to a data science role.

Business Analysts who want to enhance their data analysis capabilities and learn to apply machine learning techniques.

Graduates and early-career professionals considering a career in data science.

Entrepreneurs who want to leverage data science to drive business growth and strategic decision-making.

Our courses in London are held at the LPC office located at:

LPC Oxford Street Offices: 25 N Row, London W1K 6DJ

London Head Office: 47 - 49 Park Royal Road, London NW10 7LQ

Once you register, we will subsequently send you the course details, including the location, trainer, and other logistical information.

Pay Attention, Please! The course location at our offices is subject to availability. Should our office be unavailable, we will secure an alternative nearby venue and promptly inform you of the change. The exact time and location will be confirmed one week prior to the course commencement.

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

Data Science Diploma

Diploma Info

Available Dates

Dates in Other Venues

Diploma Details

Diploma Outline

Month 1

Week 1

Day 1: Introduction to Data Science

Day 2: Basics of Statistics for Data Science

Day 3: Introduction to Programming for Data Science

Week 2

Day 1: Data Manipulation and Analysis

Day 2: Data Visualization

Day 3: Exploratory Data Analysis

Week 3

Day 1: Introduction to Databases

Day 2: Basics of Machine Learning

Day 3: Feature Engineering and Model Evaluation

Week 4

Day 1: Supervised Learning in Depth

Day 2: Unsupervised Learning in Depth

Day 3: Introduction to Deep Learning

Month 2

Week 1

Day 1: Advanced Machine Learning

Day 2: Natural Language Processing

Day 3: Recommender Systems

Week 2

Day 1: Time Series Analysis

Day 2: Introduction to Big Data

Day 3: Data Science Project Lifecycle

Week 3

Day 1: Data Ethics and Privacy

Day 2: Advanced Deep Learning

Day 3: Reinforcement Learning

Week 4

Day 1: Deploying Data Science Models

Day 2: Cloud Computing for Data Science

Day 3: Real-time Analytics

Month 3

Week 1

Day 1: Data Science in Business

Day 2: Data Strategy

Day 3: Future Trends in Data Science

Week 2

Day 1: Industry Applications of Data Science

Day 2: Advanced Topics in AI

Day 3: Case Studies in Data Science

Week 3

Day 1: Communication and Storytelling in Data Science

Day 2: Advanced Analytics Techniques

Day 3: Special Topics in Data Science

Week 4

Day 1: Multimodal Data Fusion

Day 2: Data Science in Social Sciences

Day 3: Quantum Computing and Data Science

Search Diplomas

Related Diplomas