The Data Science Course Syllabus covers everything you will need to learn and upskill in the field of Data Science. Let us deep dive into the components you will be learning in detail.
Introduction to Data Science
In this modules, you will explore the fundamentals of data science, including the basics of machine learning, various models and algorithms, and learn how to choose and evaluate the right model for your needs. You’ll delve into supervised and unsupervised learning techniques, time series analysis, and hypothesis testing. Through hands-on projects, you’ll gain practical experience that prepares you to implement machine learning solutions effectively.
Machine Learning Models
In this section of the Data Science Course Outline, you’ll dive into the fascinating world of Machine Learning Models. You’ll discover what constitutes a machine learning model and explore various types, enabling you to make well informed choices when selecting the right one for your needs. You’ll also learn how to effectively train and evaluate your model, and uncover strategies to enhance its performance for optimal results.
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
More on Models
In this course, you’ll dive into the world of predictive modeling, starting with an understanding of predictive models and how to work with linear and polynomial regression techniques. You’ll explore multi-level models and the critical process of selecting the right model for your data. Additionally, you’ll learn about algorithm boosting, including its various types and a deep dive into adaptive boosting, equipping you with the skills to enhance model performance effectively.
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi-Level Models
- Selecting the Right Model or Model Selection
- Need for Selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
In this module of the Data Science Course Syllabus for Beginners, you’ll gain a comprehensive understanding of machine learning algorithms and their significance in the field. You’ll explore various types of algorithms, including supervised learning, unsupervised learning, and reinforcement learning, uncovering how each approach applies to real-world problems. By the end, you’ll be equipped with the knowledge to leverage these algorithms effectively in your projects.
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Exploring Supervised Learning Algorithms
In this module, you’ll explore supervised learning algorithms, mastering classification techniques like Logistic Regression, Naïve Bayes, and Support Vector Machines. You’ll also delve into Time Series Analysis, understanding its components, advantages, and models while learning to implement effective forecasting strategies.
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbor
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
Exploring Unsupervised Learning Algorithm
In this module of the Data Science Course Outline, you’ll learn about key concepts such as clustering and dimensionality reduction. Discover how K-means and hierarchical clustering algorithms work and how to implement them effectively. You’ll also uncover the importance of dimensionality reduction techniques like Linear Discriminant Analysis and Principal Component Analysis and enhance your data analysis skills.
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and Advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
In this Data Science Course Syllabus module, you’ll learn about hypothesis testing in machine learning, a vital technique for data-driven decision-making. You’ll explore the basics of hypotheses, including normalization methods, key parameters like null and alternative hypotheses, and the significance of the p-value. Additionally, you’ll discover various tests, such as the t-test, z-test, ANOVA test, and chi-square test, to enhance your analytical skills.
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
Overview Reinforcement Learning Algorithm
In this module of the Data Science Course Outline, you will learn about Reinforcement Learning (RL) algorithms, a powerful approach in machine learning. You will discover RL’s advantages, including its ability to learn from interactions with the environments and the key components that make up these algorithms. Additionally, we’ll explore the critical concept of the exploration vs. exploitation tradeoff, which is essential for optimizing learning and decision-making in dynamic situations.
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration vs. exploitation tradeoff
Hands-On Project
You’ll learn how to analyze real-world datasets, implement machine learning algorithms, and visualize your findings to drive insights. You’ll gain practical experience in data cleaning, exploratory data analysis, and model evaluation, equipping you with the skills needed to handle complex data challenges.
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Data Science and Machine Learning with R
In this section of the Data Science Syllabus, you will discover the essentials of data science and the powerful capabilities of R programming. You will learn about machine learning algorithms, data visualization techniques, and statistical analysis, all while gaining hands-on experience with practical projects. By the end, you will be equipped to tackle real-world data challenges confidently.
Introduction to Data Science
You will start with the introduction to Data Science and will explore the fundamental concepts of the field, including the Data Science Life Cycle and the roles of Artificial Intelligence (AI) as part of the Data Science Course Syllabus. You’ll gain insights into key AI components such as Deep Learning, Machine Learning, Artificial Neural Networks (ANN), and Natural Language Processing (NLP). Additionally, you’ll discover how R connects to Machine Learning and learn to leverage R as a powerful tool for implementing Machine Learning solutions.
- Understanding Data Science
- The Data Science Life Cycle
- Understanding Artificial Intelligence (AI)
- Overview of Implementation of Artificial Intelligence
- Machine Learning
- Deep Learning
- Artificial Neural Networks (ANN)
- Natural Language Processing (NLP)
- How R connected to Machine Learning
- R – as a tool for Machine Learning Implementation
Introduction to R programming
In this module of the Data Scientist Course Syllabus, you’ll learn about R programming, exploring its history, features, and the powerful R Studio environment. We’ll cover how to install R, set up your workspace, and navigate the command prompt. You’ll also grasp R programming syntax and how to work with R script files, providing a solid data analysis foundation.
R programming Basics
In this module, you’ll learn the essentials of R programming, including data types and variable management. You’ll explore various operators and master decision-making statements like IF and Switch. Additionally, you’ll gain hands-on experience with loops—Repeat, While, and For—and control them using Break and Next statements.
- Data types in R
- Creating and Managing Variables
- Understanding Operators
- Assignment Operators
- Arithmetic Operators
- Relational and Logical Operators
- Other Operators
- Understanding and using Decision Making Statements
- The IF Statement
- The IF…ELSE statement
- Switch Statement
- Understanding Loops and Loop Control
- Repeat Loop
- While Loop
- For Loop
- Controlling Loops with Break and Next Statements
More on Data Types
In this module of the Data Science Syllabus for Beginners, you’ll learn about key data types in R, starting with vectors, arrays, and matrices, including their creation and manipulation. You’ll explore lists and factors, understand how to work with data frames and perform operations like merging and subsetting. Finally, you’ll master converting and checking data types, giving you the skills needed for effective data analysis in R.
- Understanding the Vector Data type
- Introduction to Vector Data Type
- Types of Vectors
- Creating Vectors and Vectors with Multiple Elements
- Accessing Vector Elements
- Understanding Arrays in R
- Introduction to Arrays in R
- Creating Arrays
- Naming the Array Rows and Columns
- Accessing and manipulating Array Elements
- Understanding the Matrices in R
- Introduction to Matrices in R
- Creating Matrices
- Accessing Elements of Matrices
- Performing various computations using Matrices
- Understanding the List in R
- Understanding and Creating List
- Naming the Elements of a List
- Accessing the List Elements
- Merging different Lists
- Manipulating the List Elements
- Converting Lists to Vectors
- Understanding and Working with Factors
- Creating Factors
- Data frame and Factors
- Generating Factor Levels
- Changing the Order of Levels
- Understanding Data Frames
- Creating Data Frames
- Matrix Vs Data Frames
- Sub setting data from a Data Frame
- Manipulating Data from a Data Frame
- Joining Columns and Rows in a Data Frame
- Merging Data Frames
- Converting Data Types using Various Functions
- Checking the Data Type using Various Functions
Functions in R
In this module of the Data Scientist Course Syllabus, you’ll explore the fundamentals of functions in R, starting with a clear definition and the essential components that make them work. You’ll delve into built-in functions, including character/string, numerical, statistical, and date/time functions. Additionally, you’ll learn how to create and call user-defined functions (UDFs) and gain insights into the concept of lazy evaluation.
- Understanding Functions in R
- Definition of a Function and its Components
- Understanding Built-in Functions
- Character/String Functions
- Numerical and Statistical Functions
- Date and Time Functions
- Understanding User-Defined Functions (UDF)
- Creating a User-defined Function
- Calling a Function
- Understanding Lazy Evaluation of Functions
Working with External Data
In this module of the Data Science Course Outline, you’ll learn how to work with external data in R, including manipulating text and CSV files. You’ll explore how to handle Excel files and use WriteBin() and ReadBin() for binary file management. Additionally, you’ll discover how to connect to and manage MySQL databases with the RMySQL package, enhancing your data-handling capabilities.
- Understanding External Data
- Understanding R Data Interfaces
- Working with Text Files
- Working with CSV Files
- Understanding Verify and Load for Excel Files
- Using WriteBin() and ReadBin() to manipulate Binary Files
- Understanding the RMySQL Package to Connect and Manage MySQL
Databases
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Data Visualization with R
In this module of the Data Science Syllabus, you’ll learn the essentials of data visualization and how to utilize R libraries for creating impactful charts and graphs. You’ll explore various types of visualizations, including pie charts, bar charts, box plots, scatter plots, histograms, and line graphs, empowering you to convey data insights effectively.
- What is Data Visualization
- Understanding R Libraries for Charts and Graphs
- Using Charts and Graphs for Data Visualizations
- Exploring Various Chart and Graph Types
- Pie Charts and Bar Charts
- Box Plots and Scatter Plots
- Histograms and Line Graphs
Exploring Statistical Computations using R
In this module, you’ll learn the basics of statistical analysis and its applications. You’ll explore key concepts like mean, median, and mode and dive into linear and multiple regression techniques. Additionally, you’ll understand normal and binomial distributions, as well as inferential and descriptive statistics.
- Understanding the Basics of Statistical Analysis
- Uses and Advantages of Statistical Analysis
- Understanding and using Mean, Median and Mode
- Understanding and using Linear, Multiple and Logical Regressions
- Generating Normal and Binomial Distributions
- Understanding Inferential Statistics
- Understanding Descriptive Statistics and Measure of Central Tendency
Packages in R
In this module of the Data Science Syllabus, you’ll learn about R packages and their importance in expanding your programming capabilities. You’ll discover how to install and load packages seamlessly, as well as manage them effectively to keep your R environment organized.
- Understanding Packages
- Installing and Loading Packages
- Managing Packages
Understanding Machine Learning Models
In this module, you’ll learn what a machine learning model is and explore various types available. You’ll discover how to choose the right model for your needs and gain insights into training and evaluating its performance. Finally, you’ll uncover strategies to enhance your model’s accuracy.
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
More on Models
In this module of the Data Scientist Course Syllabus, you’ll learn about predictive models, including linear and polynomial regression techniques. You’ll explore the importance of model selection and delve into multi-level models. Additionally, you’ll discover algorithm boosting, its various types, and gain insights into adaptive boosting
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi Level Models
- Selecting the Right Model or Model Selection
- Need for selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Exploring Supervised Learning Algorithms
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbor
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
Exploring Un-Supervised Learning Algorithms
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and Advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
Overview Reinforcement Learning Algorithm
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration Vs Exploitation tradeoff
Hands on Project
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Data Science and Machine Learning with Python
Introduction to Data Science
- Understanding Data Science
- The Data Science Life Cycle
- Understanding Artificial Intelligence (AI)
- Overview of Implementation of Artificial Intelligence
- Machine Learning
- Deep Learning
- Artificial Neural Networks (ANN)
- Natural Language Processing (NLP)
- How Python connected to Machine Learning
- Python as a tool for Machine Learning Implementation
Introduction to Python
Python is a versatile programming languages with a rich history. In this module of the Data Science Course Outline, you’ll explore the differences between Python 2 and 3, learn to install Python and set up your environment, and understand Python’s identifiers, keywords, and indentation. You’ll also cover comments, documentation, command line arguments, user input, and basic data types and variables.
- What is Python and history of Python
- Python-2 and Python-3 differences
- Install Python and Environment Setup
- Python Identifiers, Keywords and Indentation
- Comments and document interlude in Python
- Command line arguments and Getting User Input
- Python Basic Data Types and Variables
List, Ranges and Tuples in Python
In this module of the Data Science Course Syllabus, you’ll learn to master Python lists, understanding their creation and manipulation. You’ll delve into iterators for efficient data traversal, and explore generators, comprehensions, and lambda expressions for concise coding. Additionally, you’ll gain proficiency in using ranges to handle numerical sequences effectively.
- Understanding Lists in Python
- Understanding Iterators
- Generators, Comprehensions and Lambda Expressions
- Understanding and using Ranges
Python Dictionaries and Sets
In this module, you’ll learn about Python Dictionaries and Sets, focusing on their creation, manipulation, and advanced features. You’ll explore practical examples to understand their functionality and versatility.
- Introduction to the section
- Python Dictionaries and More on Dictionaries
- Sets and Python Sets Examples
Input and Output in Python
In this module of the Data Scientist Course Syllabus, you’ll learn how to read and write text files, append data to existing files, and write binary files in Python. You’ll also discover how to use the Pickle module to serialize and deserialize Python objects. By mastering these skills, you’ll be able to manage data efficiently in your Python programs.
- Reading and writing text files
- Appending to Files
- Writing Binary Files Manually and using Pickle Module
Python Functions
In this module, you’ll explore the creation of user-defined functions and the power of built-in package functions in Python. You’ll learn how to use anonymous functions for concise coding, manage control flow with loops and statements, and effectively organize your code with modules and packages.
- Python user defined functions
- Python packages functions
- The anonymous Functions
- Loops and statement in Python
- Python Modules & Packages
Python Exception Handling
In this module of the Data Science Course Syllabus, you’ll learn the fundamentals of Python exception handling, including what exceptions are and how to manage them using try, except, and else blocks. You’ll explore the try-finally clause, discover Python’s standard exceptions, and understand how to raise exceptions and create user-defined ones.
- What is Exception?
- Handling an exception
- try….except…else
- try-finally clause
- Argument of an Exception
- Python Standard Exceptions
- Raising an exceptions
- User-Defined Exceptions
Python Regular Expressions
In this module, you’ll learn the fundamentals of Python regular expressions, including what they are and how to use them effectively. You’ll explore the match and search functions, grasping the differences between matching and searching patterns. Additionally, you’ll discover how to perform search and replace operations, as well as delve into extended regular expressions and wildcards for more advanced text manipulation.
- What are regular expressions?
- The match Function and the Search Function
- Matching vs Searching
- Search and Replace
- Extended Regular Expressions and Wildcard
Useful Additions
In this module of the Data Scientist Course Syllabus, you’ll discover the power of collections like named tuples and default dictionaries, which streamline your data management and enhance code readability. You’ll also master debugging techniques and breakpoints to efficiently troubleshoot your programs. Plus, you’ll learn how to leverage integrated development environments (IDEs) to boost your productivity and streamline your workflow.
- Collections – named tuples, default dicts
- Debugging and breakpoints, Using IDEs
Data Manipulation using Python
In this module of the Syllabus for Data Science Course at FITA Academy, you’ll learn to manipulate data using Python, covering various data types and extraction methods. You’ll manage raw and processed data, perform data wrangling, and explore statistical concepts like mean, median, and standard deviation. Additionally, you’ll delve into exploratory data analysis (EDA) and utilize libraries like NumPy, SciPy, and Pandas to enhance your data skills.
Understanding Machine Learning Models
- Understand what is a Machine Learning Model
- Various Machine Learning Models
- Choosing the Right Model
- Training and Evaluating the Model
- Improving the Performance of the Model
More on Models
- Understanding Predictive Model
- Working with Linear Regression
- Working with Polynomial Regression
- Understanding Multi Level Models
- Selecting the Right Model or Model Selection
- Need for selecting the Right Model
- Understanding Algorithm Boosting
- Various Types of Algorithm Boosting
- Understanding Adaptive Boosting
Understanding Machine Learning Algorithms
- Understanding the Machine Learning Algorithms
- Importance of Algorithms in Machine Learning
- Exploring different types of Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Exploring Supervised Learning Algorithms
- Understanding the Supervised Learning Algorithm
- Understanding Classifications
- Working with different types of Classifications
- Learning and Implementing Classifications
- Logistic Regression
- Naïve Bayes Classifier
- Nearest Neighbour
- Support Vector Machines (SVM)
- Decision Trees
- Boosted Trees
- Random Forest
- Time Series Analysis (TSA)
- Understanding Time Series Analysis
- Advantages of using TSA
- Understanding various components of TSA
- AR and MA Models
- Understanding Stationarity
- Implementing Forecasting using TSA
Exploring Un-Supervised Learning Algorithms
- Understanding Unsupervised Learning
- Understanding Clustering and its uses
- Exploring K-means
- What is K-means Clustering
- How K-means Clustering Algorithm Works
- Implementing K-means Clustering
- Exploring Hierarchical Clustering
- Understanding Hierarchical Clustering
- Implementing Hierarchical Clustering
- Understanding Dimensionality Reduction
- Importance of Dimensions
- Purpose and advantages of Dimensionality Reduction
- Understanding Principal Component Analysis (PCA)
- Understanding Linear Discriminant Analysis (LDA)
Understanding Hypothesis Testing
- What is Hypothesis Testing in Machine Learning
- Advantages of using Hypothesis Testing
- Basics of Hypothesis
- Normalization
- Standard Normalization
- Parameters of Hypothesis Testing
- Null Hypothesis
- Alternative Hypothesis
- The P-Value
- Types of Tests
- T Test
- Z Test
- ANOVA Test
- Chi-Square Test
Overview Reinforcement Learning Algorithm
- Understanding Reinforcement Learning Algorithm
- Advantages of Reinforcement Learning Algorithm
- Components of Reinforcement Learning Algorithm
- Exploration Vs Exploitation tradeoff
