Full Stack Data Science Career Path

Introduction to Data Science provides a foundational understanding of the principles and techniques essential for extracting insights from data. Mastering the fundamentals of data science, individuals gain the ability to address complex challenges, identify opportunities, and drive innovation in the real world.

• 1.1 What is Data Science?:
  • ‍Explore the interdisciplinary field of data science and its role in extracting insights from data.
• 1.2 Applications of Data Science:
  • Discover real-world applications across various industries, from healthcare to finance, where data science drives innovation and decision-making.
• 1.3 Data Science Lifecycle:
  • Understand the systematic process of collecting, preparing, analysing, and interpreting data to extract valuable insights.

Programming Fundamentals offers a comprehensive introduction to the core concepts and techniques of programming essential for data science and analysis. It covers fundamental programming constructs such as variables, data types, control structures, functions, and modules, providing a solid foundation for data manipulation and analysis. Mastery of programming fundamentals enables individuals to write efficient and scalable code, automate repetitive tasks, and develop robust data analysis solutions, making it a crucial skill set for aspiring data scientists and analysts.

• 1.1 Introduction to Python:
  • ‍Master the fundamentals of Python programming language, a versatile tool for data manipulation and analysis.
• 1.2 Data Types and Variables:
  • Learn about different data types and how variables are used to store and manipulate data.
• 1.3 Control Structures (if, else, loops):
  • Understand control structures for directing program flow and making decisions based on conditions.
• 1.4 Functions and Modules:
  • Explore the concept of functions and modules for organising code into reusable components.

Data Manipulation and Analysis is a critical aspect of data science, focusing on transforming raw data into meaningful insights. This module equips participants with the skills to clean, preprocess, and manipulate data using tools like Pandas in Python. By mastering data manipulation techniques, individuals can effectively handle complex datasets, extract relevant information, and prepare data for further analysis, enabling them to derive actionable insights and make informed decisions in various domains such as finance, healthcare, marketing, and more.

• 1.1 Working with DataFrames (Pandas):

• ‍Dive into Pandas, a powerful library for data manipulation and analysis, and learn to work with tabular data effectively.

• 1.2 Data Cleaning and Preprocessing:

• Explore techniques for cleaning and preprocessing raw data to ensure its quality and reliability.
• 1.3 Data Aggregation and Grouping: Learn how to aggregate and group data to derive meaningful insights and summaries
• 1.4 Data Visualization: Discover the importance of data visualisation and learn to create visualisations using Matplotlib and Seaborn.

Exploratory Data Analysis (EDA) and Statistics play a pivotal role in the data science workflow, providing crucial insights into the underlying patterns, relationships, and distributions within datasets. This module delves into EDA techniques such as data visualisation, summary statistics, and hypothesis testing, enabling participants to gain a deep understanding of their data and uncover valuable insights. By mastering EDA and statistics, individuals can effectively identify trends, outliers, and correlations, facilitating informed decision-making and driving impactful solutions across diverse domains including finance, healthcare, retail, and beyond.

• 1.1 Introduction to Data Visualization Libraries:
  • ‍Explore Matplotlib and Seaborn libraries for creating various types of plots such as line plots, scatter plots, histograms, etc.
• 1.2 Plotting Techniques:
  • Learn advanced plotting techniques and how to customise plots to convey insights effectively
• 1.3 Exploratory Data Analysis (EDA):
  • Dive into EDA techniques to uncover patterns, anomalies, and relationships in data.
• 1.4 Descriptive Statistics:
  • Understand descriptive statistics to summarise and describe the main features of a dataset.
• 1.5 Statistical Concepts:
  • Explore statistical concepts such as probability theory, statistical distributions, and hypothesis testing.‍

Machine Learning Basics is a fundamental module that introduces participants to the core concepts, algorithms, and applications of machine learning. Through this module, learners gain a solid understanding of supervised and unsupervised learning, model evaluation metrics, and common machine learning algorithms such as linear regression, logistic regression, decision trees, and support vector machines (SVM). Proficiency in machine learning basics equips individuals with the essential skills to build predictive models, classify data, and uncover patterns from datasets, thereby enabling data-driven decision-making and problem-solving in various domains including finance, healthcare, marketing, and more.

• 1.1 Introduction to Machine Learning:
  • ‍Understand the fundamentals of machine learning and its applications in predictive modelling and pattern recognition.
• 1.2 Supervised vs. Unsupervised Learning:
  • Learn the difference between supervised and unsupervised learning techniques and their use cases.
• 1.3 Model Evaluation Metrics:
  • Explore metrics for evaluating the performance of machine learning models.
• 1.4 Supervised Learning Algorithms:
  • Dive into popular supervised learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forests, and Support Vector Machines (SVM).
• 1.5 Unsupervised Learning Algorithms:
  • Explore unsupervised learning algorithms like K-Means Clustering and Hierarchical Clustering for grouping similar data points and discovering hidden patterns.‍

Advanced Topics delves into cutting-edge concepts and techniques in data science, exploring specialized areas beyond the basics. Participants are exposed to advanced machine learning algorithms like ensemble learning, neural networks, and deep learning, which enable them to tackle complex problems and achieve higher levels of model performance. Additionally, this module covers topics such as natural language processing (NLP), recommendation systems, anomaly detection, and reinforcement learning, providing learners with the expertise to address sophisticated challenges in areas like text analysis, personalized recommendations, anomaly detection in financial transactions, and autonomous decision-making systems. Mastery of advanced topics empowers data scientists to push the boundaries of innovation and make significant contributions to industries ranging from e-commerce to healthcare and beyond.

• 1.1 Advanced Machine Learning:
  • ‍Delve into advanced machine learning techniques such as ensemble learning, neural networks, and deep learning for solving complex problems.
• 1.2 Natural Language Processing (NLP):
  • Explore techniques for processing and analyzing text data, including sentiment analysis, text classification, and language translation.
• 1.3 Recommendation Systems:
  • Learn how recommendation systems use machine learning to provide personalised recommendations to users.
• 1.4 Model Deployment and Production:
  • Understand the process of deploying machine learning models into production environments and strategies for model deployment.

Big Data and Distributed Computing introduces participants to the fundamental concepts and technologies essential for handling large-scale datasets and performing complex computations across distributed systems. In this module, learners gain an understanding of big data technologies like Hadoop and Spark, which enable the storage, processing, and analysis of massive volumes of data across clusters of computers. They also explore distributed data processing techniques, scalable machine learning algorithms, and data engineering principles, equipping them with the skills to design and implement robust data infrastructure solutions capable of handling the volume, velocity, and variety of big data. Proficiency in big data and distributed computing empowers data professionals to extract actionable insights from massive datasets efficiently, enabling organisations to make data-driven decisions and drive innovation at scale.

• 1.1 Introduction to Big Data Technologies:
  • ‍Explore big data technologies such as Hadoop and Spark for processing and analysing large volumes of data
• 1.2 Distributed Data Processing:
  • Learn how distributed computing frameworks enable parallel processing of big data across multiple nodes.
• 1.3 Scalable Machine Learning Algorithms:
  • Explore scalable machine learning algorithms designed to handle large datasets efficiently.

Data Engineering equips participants with the knowledge and skills required to design, develop, and maintain robust data infrastructure and pipelines, ensuring efficient data processing and management. Learners delve into data pipeline development, ETL processes, data warehousing concepts, and advanced data engineering techniques, enabling them to build scalable and reliable data systems that support various data-driven applications and analytics. Additionally, the Business Skills component focuses on enhancing participants' communication, collaboration, and problem-solving abilities, preparing them to effectively communicate data insights, drive informed decision-making, and deliver measurable value to stakeholders across the organization. Proficiency in data engineering and business skills enables professionals to bridge the gap between technical analysis and business objectives, driving organizational success through data-driven strategies and initiatives.

• 1.1 Data Engineering:
  • ‍Gain an understanding of data engineering principles, data pipeline development, and ETL processes for building robust data infrastructure.
• 1.2 Business and Communication Skills:
  • Enhance your business acumen and communication skills to effectively communicate data insights and drive decision-making processes.
• 1.3 Effective Communication of Data Insight:
  • Learn to effectively communicate insights derived from data to stakeholders using storytelling techniques.
• 1.4Business Acumen and Decision-Making:
  • Understand the role of data science in driving business decisions and developing data-driven strategies for organizational success.