Data Science is a dynamic field that requires understanding large amounts of information and insights. In order to become a successful Data Scientist, it’s important to know the data scientist course syllabus. Here is the comprehensive syllabus discussed below.
Data is everything today. According to reports, every person would generate around 1.7Mb of data every second in the next few days. To handle this massive amount of data, businesses have already started to scale up their search for Data Scientist. In 2020 alone, over 6,500 data science job posts were active.
Data Science is a large field that includes a variety of topics from statistics, mathematics, and information technology. A beginner’s Data Science course syllabus covers fundamental and advanced concepts in data analytics, machine learning, statistics, and programming languages (Python or R). It also teaches students how to read massive datasets and spot trends in order to build prediction models. If you are thinking of learning data science and looking for a data scientist course syllabus, then this article is for you.
What is a Data Scientist Course Syllabus?
A Data Science program is like your compass in the huge world of data. It’s your guide to understanding different data types and the statistics. A Data Science Program is designed with a large number of methods, tools, and skills important for handling corporate data.
The data scientist course syllabus is like the world full of statistics, programming, algorithms, and various analytical techniques, gaining specialised knowledge and expertise. With the help of these courses, one can develop real-world-problem-solving abilities and decision-making. As a result, he/she will become a data science professional, who is ready to take a variety of data science roles to get hired by top companies.
Importance of Data Scientist Course Syllabus
Data science is a vast field. As a result, Data Scientists have a lot to do. A data scientist course syllabus combines Business acumen, Mathematics, Statistical models, Machine Learning techniques, and algorithms. It gives students an understanding of the fundamentals and core concepts of data science, that are important as per industry point of view. With this knowledge, data scientists can draw meaningful insights to solve complex problems. Here is the list of fundamental competencies and talents that every employer looks while hiring for data science subjects:
Introduction to Data Science: This includes fundamentals of data science, datasets, and standard techniques for exploring data.
Mathematics: Fundamentals of mathematics and statistics, probability, and linear algebra, calculus are the most important topics of data science.
Business Intelligence tools: You will be in charge of making decisions at different labels, which require different methods of collecting and managing data to gain meaningful insights. Data warehouse, integrating multiple data sources, and developing reports will play a major part.
Programming Languages: Programming languages for data science are Python and R. An overview of their syntax, basic commands, and how to use them in data analysis projects is important.
Query Language: SQL, NoSQL and MongoDB are important from basic to how to query data from a relational database.
Machine Learning, Deep Learning and Artificial Intelligence: A data scientist course syllabus contains some of the main ML, AI, DL algorithms and how to use them for solving real-world problems.
Data Mining: Data Mining will teach you about its principles and techniques used for extracting patterns from large datasets. Data analysis strategies, clustering, and reducing dimensionality are part of Data Mining.
Data Manipulation: Data manipulation and data visualization become essential for understanding data sets.
Data Visualization and Reporting: Learning about tools to visualize data are very important. R packages, Tableau, and Power BI are important.
Data Modeling, Selection, and Evaluation: Selecting the right data model and evaluating its performance is important. It helps you learn about accuracy, precision, and techniques for selecting the most appropriate model on a given problem.
Best Data Scientist Course Syllabus Topics
No matter how you are learning. Whether it’s an online course, classroom-classroom, a full-time university degree or bootcamp. The topics are going to be these only, but each course’s projects could be different. Any data scientist course syllabus must have:
| Data Scientist Course Syllabus Topics | |
| Mathematical and Statistical Skills | Foundation in math and stats |
| Machine Learning | Techniques for automated learning |
| Artificial Intelligence | Mimicking human intelligence |
| Coding | Programming proficiency |
| Applied Mathematics and Informatics | Practical math and data analysis |
| Machine Learning Algorithms | Algorithms for data analysis |
| Data Warehousing | Data storage and retrieval |
| Data Mining | Discovering patterns in data |
| Data Visualization | Visual representation of data |
| Cloud Computing | Data processing on the cloud |
| Data Structures | Organizing and managing data |
| Scientific Computing | Applying math to scientific problems |
| Scholastic Models | Theoretical models in data analysis |
| Project Deployment Tools | Tools for deploying data projects |
| Predictive Analytics and Segmentation | Making predictions from data |
| Exploratory Data Analysis | Preliminary data examination |
Data Scientist Course Syllabus Topics in Detail
Programming Languages
Studying data science requires the use of huge amounts of data and these huge amounts of data cannot always be written down on paper or in Excel sheets. This is how the use of programming languages comes in and supports it as a backbone or foundation of data science.
No data science project is complete without knowing how to instruct the computer or machine to do the work. One must know how to extract or retrieve a particular set of data from a dataset.
| Programming Languages for Data Scientist Course Syllabus | |
| Python | Primary language for machine learning and deep learning. |
| R | used for statistical analysis and data visualization. |
| SQL (Structured Query Language) | Essential for structured data querying and manipulation. |
| NoSQL | Used for managing and querying unstructured data. |
StatisticsÂ
Statistics and Mathematics are often included in the data scientist course syllabus. Inferential statistics are also important as it’s used to determine if the sample from a set is representative of the population.
| Statistics for Data Scientist Course Syllabus | |
| Descriptive Statistics | Describing and summarizing data, including measures like mean, median, and standard deviation. It helps identify outliers and missing data handling. |
| Inferential Statistics | Drawing conclusions from data, such as hypothesis testing and evaluating sample representativeness. |
| Probability | Understanding the likelihood of events occurring, a fundamental concept in statistics and machine learning. |
| Linear Algebra | Fundamental for understanding concepts like neural networks in deep learning. |
| Conditional Probability | Important for various machine learning methods. |
| Mathematical Skills for ML | Fundamental for grasping the mathematical foundations of machine learning algorithms. |
Mathematical FoundationsÂ
Linear Algebra, Calculus, Differentiation, Probability and Statistics, Vectors, and Matrices are some essential principles in the discipline of Mathematics that are important to machine learning and deep learning models. Basic knowledge and awareness of these underlying subjects are required for enhanced execution of the related algorithms.
| Mathematical Foundations for Data Scientist Course Syllabus | |
| Linear Algebra | Fundamental for understanding the structure and operations of neural networks and deep learning. |
| Calculus | Necessary for optimizing machine learning models, such as gradient descent for model training. |
| Differentiation | Integral for computing gradients in optimization algorithms, enabling model learning and adaptation. |
| Probability and Statistics | Key for understanding uncertainty, distributions, and statistical inference in machine learning. |
| Vectors and Matrices | Central for representing and manipulating data in high-dimensional spaces, prevalent in ML and DL. |
Data Analysis
All data science projects are incomplete without proper exploration and data analysis. Presenting the data in a simplified format to a stakeholder (non-technical person) is crucial for their comprehension and awareness of the information it contains. The common forms are univariate analysis, bivariate analysis, and multivariate analysis.
| Data Analysis and Visualization for Data Scientist Course Syllabus | |
| Univariate Analysis | Examining individual variables to understand their distribution, summary statistics, and patterns. |
| Bivariate Analysis | Analyzing the relationships between two variables, often through scatter plots and correlation analysis. |
| Multivariate Analysis | Exploring the interactions and dependencies between multiple variables, providing deeper insights into data relationships. |
Data Munging (Data Wrangling)
Data science is all about munging the data. Preprocessing of data depends on the data type i.e., text or numerical data. Data preprocessing also solves treating missing or null values, outliers, and transforming the variables. Listed below in detail:
| Data Munging (Data Wrangling) for Data Scientist Course Syllabus | |
| Data Pre-processing | Adaptation of data for analysis, including transforming text data into binary or creating data categories, expanding image data, handling missing values, and addressing outliers. |
| Handling Text Data | Conversion of text data into a format suitable for analysis, such as binary encoding or creating data categories. |
| Image Data Processing | Enlarging image data to increase the dataset size, particularly beneficial for deep learning models based on neural networks. |
| Treating Missing Values | Identification and handling of missing or null values in the dataset through imputation or removal. |
| Outlier Treatment | Addressing outliers in the data to prevent them from skewing analysis results. |
| Variable Transformation | Altering variables or features to improve their suitability for analysis or modeling purposes. |
Machine Learning (ML)
In contrast to other concepts, machine learning is very important, challenging, and time-consuming to learn in the data scientist course syllabus. ML applies various statistical tools to make predictions, recommendations and suggestions based on the problem statement. ML widely uses vectors and matrices to make the dataset look easier.
| Machine Learning (ML) for Data Scientist Course Syllabus | |
| Integral Data Science Element | It applies statistical tools to make predictions and recommendations. |
| Diverse Applications | Machine Learning encompasses a wide array of applications, enhancing the complexity of models, and is divided into types based on data type and the applicable algorithms for different scenarios and problems. |
| Vectors and Matrices | This helps in simplifying data manipulation and is particularly valuable for studying neural networks in data science. |
Deep Learning
Machine Learning is the superset of Deep learning. Its models are complex because they use a hierarchy of simpler concepts. It’s a three-step process of processing the data, learning the patterns from it, and then predicting the output. Widely used for unstructured text, images, and audio data.
Big Data
Educating students about big data aims to familiarize students with the tools, techniques, extracting hidden patterns from it and strategies for handling big data and unstructured data. Big Data deals with huge volumes of data and that is mostly unstructured in the form of clicks, videos, orders, messages, photographs, postings, etc.  Â
ML Ops, Data Dashboards and Storytelling
ML Ops, is short-form for Machine Learning Operations, is a set of practices and tools aimed at automating and streamlining the deployment, monitoring, and management of machine learning models in production environments. It is crucial to build models and execute them in order to solve the business problem.
Lastly, A data scientist’s job description includes more than just extracting, analyzing, and developing models from raw data. It also involves presenting the findings and inferences with proper documentation. Tools like Tableau and Power BI play an important role in preparing dashboards and storytelling.
Eligibility Criteria of Data Science Course
If you are seeking for a classroom program for:
Masters Program:Â a bachelor’s degree (mathematics, computer science, computer applications, or something comparable of at least 3 years) is a must with at least 50% marks undergraduate level.
Bachelors Program: Basic knowledge of math, computer science, and statistics with 50% marks in high school.
Note:Â For students who have non-technical backgrounds and starting a Data Science course,Â
prior experience with simple analytics tools like SQL, Excel, or Tableau can be beneficial.
If you are seeking for an online program say Full Stack Data Science Pro] there is no, programming experience is required. As this course will start everything from scratch.
To sum up, the Data scientist course syllabus is essential for anyone hoping to work in the field of Data Science. It’s an ever-evolving field, so one needs to stay updated on the latest data trends and technologies. Interested one should seek data science-related courses, conferences, etc. for a better understanding of data science principles and techniques. Moreover, you can visit PW Skills, your one-stop-solution for upscaling.
Data Scientist Course Syllabus FAQs
Is data science a difficult course?
Yes, data science is a difficult course because it requires a solid foundation in math, statistics, and computer programming.
Is data science a safe career?
Data science is a safe career to work in. Moreover, it's a promising career path with increasing demand for data-driven insights.
Is Python for data science hard?
Python's an easy programming language to learn, might be difficult for some in terms of how to apply Python for data science, etc.
Can I do data science if I am weak in math?
While a strong math background can be a plus-point, it's possible to pursue data science with weaker math skills. Being a data scientist is more about knowing how to solve problems and communicate them than doing math.
How long is a data science course?
Data science course duration spans between 6-12 months.
