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 are live in 2020 alone. Here, I will elaborate on the syllabus of Data Science courses, its eligibility, and the taught subjects.
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 date; 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 enable 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.
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.
In my article on how to learn Data Science from scratch, I gave you an overview of the various concepts, models, and learning techniques. Now, let’s understand the skills that are taught throughout the Data Science course syllabus.
Related: Data Science Skills Survey 2020
Data Science syllabus can be divided into two categories: 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 skills and hard skills is exactly what enterprises are looking for in their in-house data scientists.
Soft Skills in Data Science Syllabus
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 towards 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:
Communication skills and a problem-solving attitude forms the crux of this job requirement. Even if you learn all the tools and technicalities, you will achieve very less if your soft skills are not polished. So, let’s begin with soft skills that you must include in your Data Science syllabus.
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 accurate. Critical thinking is not something you mug up. It is about having a different perspective and ability to understand 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.
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.
You can be amazing with data, but if you cannot effectively communicate your ideas and analogies, it is a massive let down. 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, make sure you work on your communication skills.
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 Car;y 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 in-hand.
These skills, to some extent, depending on how you are as a person. If you really want to make a career in Data Science and want to learn all the hard skills, make sure 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.
Subjects in a Data Science Syllabus (Hard Skills)
A Data Science course syllabus consists of four major subject matters – Foundation blocks, Machine Learning, Text Mining and Natural language Processing, and Big Data Analytics.
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 with Python programming language or 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 as well as through non-electronic means. On the other hand, data manipulation is the method of altering data to make it easier to read or consume or organize. For instance, organizing a data log alphabetically is an instance of data manipulation.
- Data wrangling and summarization: Data wrangling, also termed as 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 that 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 in predicting 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 a fundamental concept in any Data Science course syllabus. It is a powerful tool and is 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
- 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 used for predictive analysis. Students learn how to parse a text to remove words, also called as 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 programming a computer to process and analyze human language data.
- Supervised & unsupervised text classification: Supervised text classification aims at classifying a text based on the pre-fed references. In contrast, unsupervised text classification aims at using 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 consisting 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 of:
- 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, which are 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. From big data infrastructure to all valuable components of big data – everything comes under this section.
- 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 any friction.
- Cloud Computing: 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 overviews or no overview at all. However, there is one thing constant – you must have a strong liking for mathematics and statistics, and computer programming. To put data, scientist eligibility more precisely, check the next section.
Eligibility for a Data Science Syllabus
For a master’s degree, you must have a bachelor’s degree in one of the relevant disciplines – mathematics or computer science or computer applications or equivalent.
If you are a beginner, having a science background helps. If you have a quantitative background like finance or business management, you can opt for a data science career. For students with non-technical backgrounds, prior knowledge with basic analytics tools like Excel or 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. If you are comfortable with the basic concepts like if-else, functions, programming logics, and loops, you are good to go.
I have already debunked the myth that coding is essential for a data science career. Here are a few more frequently asked questions that we’ll cover for you.
Frequently Asked Questions – answered for you
1.What is the data science course duration?
While universities have 2-3 years’ long degree programs, online courses can be completed in 3-6 months. For instance, the Data Science Specialization Course at AnalytixLabs is 500 hours long, effectively including all subject matters of any typical data science course.
2. What is the salary of Data Scientists?
According to Glassdoor, Data Scientists in India have a pay of INR 950k/year. Globally, the salary range for Data Scientists is on the higher end.
3. Is Data Science hard?
It depends on you. What may be hard for others may not be that hard for you. If you are comfortable with mathematics, stats, and logical thinking, you are good to go.
4. Is data scientist a good career?
Yes. Most definitely, but only if you can grasp all the soft skills and syllabus matters. Remember, it is about the practical implementation that will determine your success.
5. Is Data Science in demand?
Yes. The demand for good data scientists will only increase. Follow the report here to know more.
I hope in this article I have answered all your questions. This is where your journey to becoming a successful data scientist begins. Visit AnalytixLabs to get started with online and on-campus courses on Data Science. All the best to you.