Data Science Course Syllabus For Beginners

Today, data is everything. Reports show that over 1.7Mb of data will be created by every person every second in the coming days. And to handle this humongous data, enterprises have already started to ramp up their search for in-house Data Science experts, so much that over 6,500 data science job postings were live in 2020 alone.

Data Science is a vast field comprising many topics of Statistics, Mathematics, and IT. A Data Science course syllabus for beginners covers basic and advanced concepts of data analytics, machine learning, statistics, and programming languages like Python or R. It also teaches students how to interpret large datasets and identify patterns to create predictive models.

A good beginner’s data science course will also include topics such as

database management systems
visualization techniques
natural language processing (NLP)
cloud computing
data security.

Students are also introduced to ethical considerations related to data privacy and best practices for using datasets responsibly.

Also Read: Data Science Tutorial for Beginners: Definition, Components, and More.

If you are thinking of pursuing a data science course and looking for a data scientist course syllabus, then this article is for you.

Here, I will elaborate on the syllabus of Data Science course for beginners, the subjects taught, and eligibility to enroll for a data science course.

Table of Contents

What is Data Science?

In my previous blog – “What is Data Science?” I discussed what Data Science means and why you should consider a career in Data Science.

Data Science has come a long way. Data Scientists were once referred to as ‘business problem solvers’ who knew how to make sense of incoherent data clusters. Fast-forward to the present, Data Scientists are the most important resources for any business looking to thrive in this mad rush. They are now the ‘wizards of all problem solvers.’

This is the primary reason the syllabus of Data Science courses includes concepts that touch base on cloud computing, big data, natural language processing, and data sentiment analysis.

A Data Scientist is responsible for deriving sensible outcomes from large data sets and enabling a business to make the right decision. These business decisions can be anything – from deciding whether to sell a new product chain or not to evaluating if a UI/UX change is required for an online business.

Importance of Data Science Course

A data science course is a launchpad for starting or transitioning your career in data science. It combines business acumen, Mathematics, Statistical models, Machine Learning techniques, and algorithms.

It provides learners with an understanding of the fundamentals and core concepts of data science, which are essential for working in any industry.

A comprehensive data science course syllabus equips learners with the knowledge and skills to analyze large amounts of data quickly and accurately, spot patterns in data sets, and use predictive analytics to make informed decisions.

With this knowledge, they can draw meaningful insights and develop practical solutions to complex problems.

AnalytixLabs offers a course on data science – Data Science 360 Course covering the entire data science course syllabus from Python for Data Science, Machine Learning, Text Mining, and ML Ops. The course includes multiple case studies, assignments, and projects for hands-on experience.

What is the Syllabus of Data Science?

Whether you want to opt for an online course or a classroom course or go for a full-time university program, the syllabus of Data Science remains the same, more or less everywhere. Projects may differ in each course. However, the core concepts of Data Science are mandatory for any Data Science course syllabus.

Also Read: Top [and highly rated] Data Science Courses of 2023

The syllabus of data science consists of the following topics:

Introduction to Data Science: The fundamentals of data science include types of datasets and standard techniques for exploring data.
Programming Language: Python and R are essential data science programming languages. An overview of their syntax, basic commands, and how to use them in data analysis projects is included.
Query Language: Learn the basics of Structured Query Language (SQL) and how to query data from a relational database. You will also better understand other query languages, such as NoSQL and MongoDB.
Statistical Foundations for Data Science: Explores basic concepts of statistics and probability to develop an understanding of how to apply them for data analysis projects.
Mathematics: Fundamentals of mathematics and statistics, including linear algebra, calculus, and probability.
Exploratory Data Analysis: Fundamentals of data exploration and analysis. It covers different techniques for cleaning and preprocessing data and methods for identifying patterns and correlations in datasets.
Data Mining: Introduces the principles of data mining and covers a range of techniques used for extracting patterns from large datasets. It also focuses on developing data analysis strategies, clustering, and reducing dimensionality.
Machine Learning Techniques & AI: Understand the fundamentals of Artificial Intelligence (AI), machine learning (ML), and deep learning (DL), and how to use them for solving real-world problems.
Data Modeling, Selection, and Evaluation: Learn to select the right data model and evaluate its performance. It includes understanding metrics such as accuracy, precision, and recall, as well as techniques for selecting the most appropriate model based on a given problem.
Data Visualization and Reporting: Various techniques and tools can be used to visualize data effectively. You will gain insights into visualizing data using R packages, Tableau, and Power BI.
Business Intelligence tools: Different methods of collecting and managing data to gain meaningful insights. Topics include setting up a data warehouse, integrating multiple data sources, and developing reports with drill-down capabilities.
Big Data & Real-Time Analytics: Explore tools and techniques used to process, store and analyze large amounts of data in real-time, such as Hadoop, Spark, and NoSQL databases. You will learn about distributed computing frameworks, streaming analytics platforms, and other big data technologies.

Main Components of Data Science Course Syllabus

Let’s look in detail at each of the data science subjects, which entails the data scientist course syllabus:

Programming Languages

Programming is the backbone or foundation of data science. No data science project can see its daylight without knowing how to instruct the computer or machine to do the work. It is an essential element in the data science course syllabus bucket list.

You must know how to extract or retrieve a particular set of records from a dataset to perform the necessary actions on it. The in-demand programming language for machine learning and deep learning is Python. It is an open-source scripting language that is easy to interpret. Along with data extraction, you must also know how to query and connect to a database. SQL is the mandatory query language for structured data, and NoSQL is for unstructured data.

Statistics for Data Science

Next on the checklist are statistics and mathematics. Statistics forms the basis of all the algorithms and techniques in Machine Learning and Deep Learning. It is paramount to know how the data looks today in its present form and for which descriptive statistics are needed. Descriptive Statistics describes the data, such as the average price of a product; it further informs how the data is spread across average, if any, extremely large values. In other words, outliers exist in the data, and how the data must be treated when presented with missing values.

Inferential statistics are used to determine if the sample from a set is representative of the population. Statistics provide various evaluation metrics, and the primary aim is to test the hypothesis or assumption.

Mathematical Foundations for Data Science

Some important concepts in the discipline of Mathematics, such as Linear Algebra, Calculus, Differentiation, Probability and Statistics, Vectors, and Matrices, are fundamental to machine learning and deep learning models. For better application of the respective algorithms, it is needed to have the basic knowledge and understanding of these foundational topics.

Exploratory Data Analysis

No data science project is complete without proper exploration and analysis of the data. It is important to present the data in a condensed form to a stakeholder and for one’s understanding and knowledge of what the data conveys. The common forms of visualizing data and its variables are univariate analysis, bivariate analysis, and multivariate analysis.

Data Munging or Data Wrangling

Another crucial step in the data science life cycle is to munge the data. The data pre-processing steps depend on the data type, whether text or numerical data. If text data is converted to binary, various categories of the data are created. Image data is recreated for more data points as deep learning models based on neural networks work efficiently on larger datasets. Data preprocessing also involves treating missing or null values, treating outliers, and transforming the variables.

Machine Learning

One of the most important, challenging, and time-consuming subjects in the data scientist syllabus (apart from programming) is learning Machine Learning. Without machine learning, data science is incomplete because it applies various statistical tools to make predictions and recommendations or suggestions based on the problem statement.

Machine Learning is where all the other components of data science come into play at once and can increase the complexity of the model. It is branched into types of machine learning based on the data type. It determines which algorithms will be applicable in what scenario and problem.

ML Ops

The next important step after employing the methodologies for building models is to implement those models, known as Model Deployment or ML Ops. It is not only enough to build models but also to execute them and then only can solve the business problem.

Data Dashboards and Storytelling

A data scientist’s job profile is not limited to extracting, analyzing, and building models from the raw data. It also consists of presenting the results and inferences with proper documentation of the entire process from end to end. Tools such as Tableau and Power BI are used extensively for preparing dashboards and storytelling.

Deep Learning

Deep learning is the subset of Machine Learning. Deep learning models are complex as these are represented using a hierarchy of simpler concepts. It uses neural networks to process the data, learn patterns from it, and then predict the output. Biological neural networks inspire neural networks. These complex models require large amounts of data for processing and training. Deep learning is mostly used for unstructured text, images, and audio data.

The primary difference between Machine Learning and Deep learning is that the deep learning models learn the hidden patterns and features present in the data. Whereas in the machine learning models, the data scientist determines the features.

Big Data

Big Data deals with huge volumes of data and that is mostly unstructured. Big data comprises data gathered from various sources such as text, audio, and images. Introducing big data in data science is to familiarize one with the tools, techniques, and strategies for handling big data and unstructured data. The aim of data scientists with big data is the same as extracting hidden patterns from the data.

The Data Science syllabus can be divided into Soft Skills and Hard Skills.

Soft skills include behavioral skills that help you put your idea on the table with sufficient explanation and convincing.
Hard skills teach you to use all the tools and techniques to derive results from huge data sets.

A perfect amalgamation of soft and hard skills is what enterprises seek in their in-house data scientists.

Data Science Subjects

The below-mentioned data scientist syllabus covers in-depth all the data science subjects topic-wise. Following are the subjects in data science that form the backbone of the data science course syllabus.

A Data Science course syllabus consists of four major subject matters – Foundation blocks, Machine Learning, Text Mining, Natural language Processing, and Big Data Analytics.

Foundation Blocks

The foundation rocks are Python and R. While Python programming language is the shining star of any Data Scientist course syllabus, R is referred to as the lingua franca of Data Science, i.e., a language that has been adopted as a common programming language. Any Data Science syllabus will be either in Python programming language with R or both. These two are the backbone of your data science course, but your foundation blocks are:

Data handling and manipulation: Data handling is a process to ensure that data is safely stored or archived, or disposed of securely once the research concludes for any project. This includes developing stringent policies and methodologies to manage data handling digitally and through non-electronic means. On the other hand, data manipulation is altering data to make it easier to read, consume, or organize. For instance, organizing a data log alphabetically is an instance of data manipulation.

Data wrangling and summarization: Data wrangling, also called data mugging, involves transforming and mapping data into another format from one ‘raw’ form. The purpose is to make the data appropriate and valuable for various uses. As the term suggests, data summarization is a conclusion you write down at the end of the code, declaring the final result. This comes in handy in data mining. This summary includes insights that indicate if the data is valuable or not.

Descriptive analytics and visualization: Descriptive analytics help predict changes in a range of historical data. It helps in understanding such changes better. Data visualization is the power to create a visual representation of the data in various forms like bars, charts, lines, etc.

Machine Learning Skills

Machine learning is a key component of any Data Science syllabus. It involves mathematics and algorithm models to help students understand how a machine learns and adapts to everyday changes.

Fundamental statistical concepts: Statistics is fundamental in any Data Science course syllabus. It is a powerful tool mostly used to perform technical data analysis. There are mainly five basic statistic concepts that all data science courses cover:
- Statistical features
- Probability distributions
- Dimensionality reduction
- Over and undersampling
- Bayesian Statistics

You may also like to read:
1. Top 25 Data Science Books – Learn Data Science
2. What Is Data Science Process and Its Significance?
3. Is Data Science Hard Or Easy? How to Start a Career in Data Science

Communication skills and a problem-solving attitude form the crux of this job requirement. Even if you learn all the tools and technicalities, you will achieve very little if your soft skills are not polished. So, let’s begin with soft skills you must include in your Data Science syllabus.

Critical Thinking

Critical thinking forms an important and interesting crux of being a data scientist. As a Data Scientist, you must know how to look at a problem, frame appropriate questions, and understand how the results will transcend to business or into actionable items to pick up next. You are required to objectively analyze deeper than usual, create hypotheses, and predict results close to accuracy. Critical thinking is not something you mug up. It is about having a different perspective and understanding what resources are critical to solving the problem. Your opinions will be data-driven, and you must be taken into consideration all angles of the problem. Your key to developing this ability is curiosity.

Curiosity

A Data Scientist must be curious intellectually. You will need to ask questions that are overlooked in general. Your drive to search for answers with available data sources will set you apart. As a Data Scientist, you will never settle for ‘just enough’ because you are a creative thinker and always want to know more.

Effective Communication

You can be amazing with data, but it is a massive letdown if you cannot effectively communicate your ideas and analogies.

A Data Scientist must have the confidence and elocution power to put all ideas on the table, discuss and justify all research, theories, and hypotheses, and effectively communicate their findings to technical and non-technical audiences. To be a successful Data Scientist, work on your communication skills.

Business Acumen

Your primary role as a Data Scientist is to deliver valuable insights from data. Unless you are in academia, business acumen is a vital soft skill. Every business has one goal – to drive profit, and for that, they need valuable details and accurate predictive business patterns from the data they capture.

Your sharp business acumen will put you in a position to determine what performance models to apply and what kind of projects will catalyze the business from a financial perspective. To acquire this soft skill, you will need to focus on how a business functions, the financial key points, and what the competition is like.

Problem-Solving Attitude

Last (but not least), your attitude will determine how good you are as a Data Scientist. You will need to demonstrate your zeal to solve the problem no matter what. This, along with critical thinking, will lead you to become a successful data scientist.

As Cary Fiorina says – If you torture the data, it will confess everything. What you need is to have the patience and determination to utilize data and make a way to solve the problem at hand.

These skills, to some extent, depending on how you are. If you want to make a career in Data Science and learn all the hard skills, ensure you work on your soft skills.

Now, let’s see the real picture. Hard skills in Data Science Syllabus are the subjects that all major courses include in their syllabus for Data Science.

Data Science Course: Eligibility

For a master’s degree, you must have a bachelor’s degree in one of the relevant disciplines – mathematics, computer science, computer applications, or equivalent.

If you are a beginner, having a science background helps. You can opt for a data science career if you have a quantitative finance or business management background.

For students with non-technical backgrounds, prior knowledge of basic analytics tools like Excel, SQL, or Tableau can be of great help in getting started with a Data Science course. For more details, follow our guide on how to get started for a Data Science career.

Data Science and coding

Not knowing to code is not a problem for anyone considering a data scientist career. It may be an add-on because it will make you more comfortable with the course materials, but not essential to kickstart your data science career. You are good to go if you are comfortable with the basic concepts like if-else, functions, programming logic, and loops.

I have already debunked the myth that coding is essential for a data science career. Here are a few more frequently asked questions we’ll cover for you.

Well-known Books for Data Scientists

Some of the popular books for Data Scientists are as follows:

Conclusion

Upon completing the introductory data science course syllabus, learners will gain a foundational understanding of data science principles and techniques. It empowers them to make data-informed decisions and develop their data skills. With the right resources and dedication, students can become experts in data science and use data to make a real impact.

Data science is an ever-evolving field, so data scientists must stay updated on the latest data trends and technologies. Learners should seek out data science-related courses, conferences, or even professional certifications that will help them further to increase their knowledge of data science principles and techniques.

Frequently Asked Questions

1. Is Data Science work easy?

Data science is not extremely difficult to learn; however, it largely depends on the individual. Data Science requires a wide range of skills and knowledge, such as statistics, mathematics, programming, problem-solving, communication, and visualization.

It also requires an in-depth understanding of data principles and techniques. Many resources available can help make learning these skills easier, but they will still require dedication and effort to master.

Additionally, it is essential to note that Data Science is an ever-evolving field with constant technological changes and new algorithms being developed daily. This means staying up-to-date with the latest trends and developments to continue making progress in your work as a data scientist.

All of these factors combine to make Data Science challenging but rewarding work. Anyone can become a successful data scientist with the right resources and dedication. So if you want to make strides in your data science career, start by familiarizing yourself with the principles of Data Science and learning how to apply them.

2. Is Data Science a hard skill?

Data Science is not a hard skill, but it takes a lot of dedication and hard work to acquire the necessary skills and knowledge. Data Science involves understanding complex concepts such as machine learning, statistics, artificial intelligence, programming languages, databases, data visualization tools and techniques, predictive analytics, natural language processing (NLP), and more.

Becoming proficient in these areas requires significant time and effort. In addition to mastering all the technical aspects of Data Science, a successful Data Scientist must also have strong analytical skills and communicate effectively with non-technical colleagues.

3. Is Data Science in demand?

Data science is highly in demand, among the most rapidly growing fields in the world today. As businesses increasingly move to digital models and turn to technology for data analysis and decision-making, the demand for data scientists has skyrocketed.

According to Glassdoor, job postings for data scientist roles are among the top-rated opportunities. Data scientists bring immense value to organizations as they can help companies uncover valuable insights from their data that can be used to optimize processes, improve customer experiences, make better decisions, or even identify new markets or products.

4. Is data science the future?

Yes, data science is undoubtedly the future. It has become increasingly important in an era where digitalization and automation transform business. Data science is also transforming how businesses operate with its ability to provide predictive analytics, which helps organizations make smarter decisions faster.

With the growing demand for data analysts in almost every industry, it is becoming increasingly attractive for students to pursue a degree or certification in this field.

5. What are the prerequisites for a data science course?

Prerequisites for Data science are having an interest in digging, cleaning the data, analyzing and visualizing the data, and wanting to make sense of the data in how to use it. Apart from this, one can pursue data science courses if one has a background in quantitative fields such as mathematics, statistics, and computer science.

6. What are the eligibility criteria to pursue or start Data Science?

The eligibility criteria to pursue or start Data Science includes an undergraduate or graduate degree in mathematics, computer science, or engineering with a good knowledge of statistics and algorithms is required.

Additionally, expertise in coding languages such as SQL, Java, and Python is highly desirable. In addition to the technical qualifications, having strong analytical skills and problem-solving abilities can be advantageous when starting in data science. Lastly, the experience of working with large datasets and databases is also beneficial for getting started in data science.

7. Which subjects must I study for data science?

The data science syllabus involves having knowledge of these topics across various domains:

Computer Science
Statistics and Probability
Mathematics
Data Analysis
Data Modeling
Big Data
Machine Learning
Deep Learning
Data Visualization
Business Intelligence

8. Is having a degree in Computer Science mandatory for data science?

No, a degree in Computer Science is not mandatory for data science. Many professionals enter the field of data science without formally studying Computer Science. However, a strong computer science and programming background would certainly be beneficial.

Data scientists need to know and understand databases, algorithms, distributed computing platforms, coding languages, predictive analytics tools, and machine learning techniques – which would require some technical competency that could be acquired through formal or informal education.

9. Is Mathematics required for Data Science?

Having a degree in Mathematics is not compulsory for data science. However, concepts such as Linear, Algebra, Calculus, Probability, and Statistics form the core of data science, machine learning, and deep learning models. Not knowing these topics can make your data science journey difficult.

You may also like to read:
1. Top 25 Data Science Books – Learn Data Science
2. What Is Data Science Process and Its Significance?
3. Is Data Science Hard Or Easy? How to Start a Career in Data Science

Related: Fundamentals of Statistics for Data Science

Statistical analysis and modeling methods: Statistical analysis will teach you to generate statistics from any stored data and analyze it to derive useful information about the underlying dataset. A statistical model is a mathematical representation of the observed data. Most statistical analysis techniques fall into two categories:
- Supervised machine learning that includes regression models and classification models
- Unsupervised machine learning that includes clustering algorithms and association rules

Text Mining and NLP

Text Mining or Text Analytics uses Natural Language Processing (NLP) to convert unstructured texts in the database and documents into normal and structured data that can be analyzed or used to drive machine learning algorithms. Concepts covered in this subject area:

Handling unstructured text data: Students learn how to handle texts with no pre-defined formats using text mining techniques.

Tokenization and vectorization of text data: Any text data requires preparation before being used for predictive analysis. Students learn how to parse a text to remove words, also called Tokenization. Then they are taught to encode these words as integers or floating-point values to use as inputs for a machine-learning algorithm. This is called vectorization.

Natural Language Processing: NLP is a branch of AI that catalyzes interactions between humans and computers. Students learn to program a computer to process and analyze human language data.

Supervised & unsupervised text classification: Supervised text classification aims at classifying a text based on pre-fed references. In contrast, unsupervised text classification uses machine learning software to determine an appropriate label for the text.

Sentiment analysis of social media data: Students learn how to use a data set of social media posts to detect the user sentiment associated with that post and label it as positive or negative using machine learning.

Big Data Analytics

Unlike popular opinions, Big Data Analytics is an important component in a Data Science syllabus. Big data analytics enables students to analyze large data sets and uncover correlations, patterns, and other important insights. This subject area comprises:

Relationship database management: Relationship Database Management or RDBMS is a common database where all data is stored in tables. Modern databases have multiple tables or relations, further divided into rows and columns.

Understanding of Big Data Ecosystem: Big Data ecosystem is particularly vast. This section of the syllabus aims at familiarizing you with the multivarious technologies that exist to harness data. Everything comes under this section, from big data infrastructure to all valuable big data components.

PySpark for streaming and scalable machine learning: You learn to build a structured stream in PySpark with Databricks while side-by-side learning about efficient algorithms to scale machine learning.

Cross-platform NoSQL system: Learn about deploying a multi-platform NoSQL database to move data between different operating systems, cloud infrastructures, and servers without friction.

Cloud Computing: The last section deals with managing data stored in the cloud. Cloud computing mainly refers to the availability of computer resources to store data in the cloud. Here you learn about data centers and how to manage them.

These are a few subject matters that are important and present in mostly all data science syllabuses, whether you opt for an online data science course or an on-campus degree course.

Whatever mode of studying you pick up, the eligibilities remain constant to a large extent. While on-campus courses require strict mathematics and statistics courses, many online courses welcome students with basic or no overviews.

However, one thing is constant – you must strongly like mathematics, statistics, and computer programming. To put data and scientist eligibility more precisely, check the next section.

Subject	Topics
Introduction to Data Science	Introduction to Analytics & Data Science Introduction to Data Analytics Introduction to Business Analytics Understanding Business Applications Business Intelligence (BI) and BI Tools Business Understanding and Acumen Problem Statement Solving Techniques Research Methodology Data types and Data Models Types of Data Structured Data Semi-structured Data Unstructured Data Type of Data Analytics Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Cognitive Analytics Type of Business Analytics Evolution of Analytics Data Science Components Data Scientist Skill Set Fundamentals of Data Science Introduction to Google Colab/Kaggle workbooks
Exploratory Data Analysis	Data Structures & Algorithms Exploratory Data Analysis Data Manipulation Data Wrangling Univariate Data Analysis Bivariate Data Analysis Multivariate Data Analysis Data Mining Applied Data Analytics
Statistics	Introduction to Basic Statistics Descriptive Statistics Probability and Probability Distribution Inferential Statistics Hypothesis Testing Introduction to Sampling Statistical Modeling Types of Distributions Categorical Data Analysis Statistical Quality Control Analytical Tools for Statistics Stochastic Processes and Models Linear Regression Models Nonparametric & Nonlinear Regression Models
Mathematics	Introduction to Mathematical Foundations Numerical Analysis Calculus Differentiation Computational Mathematics Linear Algebra Vector and Matrices
Programming and Query Languages	Programming language: Python, R Python for Data Science Python Packages and Libraries Query Language: SQL Database Management SAS Programming for Analytics
Machine Learning	Types of Machine Learning Supervised Learning Regression Models Linear Regression Logistic Regression Classification Models Model Evaluation Metrics Decision Tree Random Forest Naive Bayes K-Nearest Neighbors Support Vector Machines Ensemble Techniques (Random Forest, Bagging, Boosting) Unsupervised Learning Segmentation using Clustering K-means Clustering Agglomerative Clustering Hierarchical Clustering Spectral Clustering (DBSCAN) Dimensionality Reduction Principal Component Analysis Singular Value Decomposition Market Basket Analysis Apriori algorithm Association Rule Mining Reinforcement Learning Recommendation Systems Popularity-Based Recommendation System Content-Based Recommendation System Hybrid Recommendation System Collaborative filtering: User-User collaborative filtering Item-Item collaborative filtering Time Series Analysis Simple Moving Average Exponential smoothing Model building using ARIMA, ARIMAX, SARIMAX Time series analysis techniques Forecasting Regularization Bias-Variance Tradeoff Feature Learning Techniques to improve the Machine Learning model Model Deployment
Deep Learning	Basics of Neural Networks Perceptrons Multi-Layer Perceptron Forward and Backward Propagation Gradient Descent Algorithm Loss Function Activation Functions Optimizers Supervised deep learning Artificial Neural Network (ANN) Perceptron (Single and Multi-Layer) Convolution Neural Network (CNN) Recurrent Neural Network (RNN) Variations of RNN: Long-Short Term Memory (LSTM) Gated Recurrent Unit (GRU) Unsupervised deep learning Autoencoders Deep Belief Networks Boltzmann Machine Restricted Boltzmann Machine Generative adversarial networks (GANs) Encoder-Decoder Model (Seq2Seq Models) Attention Models Transformers R-CNN and its variations: Fast R-CNN Faster R-CNN Masked R-CNN Graph Neural Networks Deep Learning in Natural Language Processing (NLP) Transfer Learning Techniques to improve the Deep Learning model Image, Text, and Audio Processing Deep Learning Frameworks such as Keras, TensorFlow, PyTorch
Text Data	Text Mining Natural Language Processing Natural Language Understanding Natural Language Generation Machine translation Language detection & Translation (Google translator) Text Recommendations Chatbots/VoiceBots/Personal Assistant systems Vectorization Countvectorizer TF-IDF Vectorizer N-Grams Word Embeddings Word2vec Glove Sentiment Analysis (Twitter feeds, reviews, feedback et al) Intent Analysis (Analyzing customer reviews) Email Classification (Google email classification – Primary-Social-Promotions-SPAM etc) Text Summarization (Google News) Fake news identification Social Network Analysis (community detection) Optical Character Recognition (OCR) Text recommendations/suggestions (Email replies, autofilling, message replies) Text Association Analysis Topic Modeling Latent Dirichlet Allocation (LDA) Latent Semantic Allocation (LSA) Non-negative Matrix-Factorization (NNMF) Speech to Text Automatic Text Generation Information Retrieval Information Extraction
Image & Audio Data	Computer Vision Image Processing and Analytics Image Augmentation Image Classification Image Segmentation Image Localization Image Captioning (Generating image titles) Image Tracking Object Detection or Identification YOLO Image and Video Analytics IoT Spatial Analytics Pattern Recognition Audio Classification Speech Recognition
Big Data	Big Data Fundamentals Data Warehousing Hadoop PySpark NoSQL Big Data Analytics Data Lakes Cloud Computing
Data Visualization & Storytelling	Data Visualization and Interpretation Power BI Tableau
ML Ops	Introduction to ML Ops Deployment of ML Model in the Cloud

Skills required for Data Science

Besides the abovementioned subjects in data science, you must also understand the skills needed to pursue a course in Data Science. The skills required for data science are bucketed into two segments:

Technical Skills [Hard Skills]

For the technical skills, we have already seen above that a sound foundation in the following data science course subjects is required:

Python Programming
SQL
Data Wrangling and Manipulation
Probability and Statistical Analysis
Mathematics
Machine Learning
Deep Learning
Data Visualization
Big Data
Cloud Computing
Ability to handle and work with unstructured data

Non-technical Skills [Soft Skills]

Many courses tend to miss out on elaborate sessions in developing soft skills. However, these soft skills are an important part of any syllabus of Data Science. Acquiring these skills is a step toward becoming a Data Scientist. If you look at any Data Science job posting, you will always find the requirements of soft skills like problem-solving, business communication, critical thinking, adaptability, etc.

For instance, see the Data Science requirements for this job role with PayPal: