Learning paths refer to a list of specific courses related to a particular profession or career interest. The path that you choose will depend directly on the skill sets that you will need to succeed in your respective career. Mastering and developing skills in the field of Data Science is the Data Science Learning Path. It is a high-in-demand path which many students are embarking upon to reach their goal of becoming Data Scientists.
The highly-structured module offers students a collection of resources that are comprehensive and valuable benefitting both professionals and those entering the field for the first time. For a student not familiar with Data Science or its learning path, it is easy to get confused by the options available. This blog post is designed to help you understand the basics to clear your confusion and help you make an informed decision.
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What is Data Science?
Curious to know What is data science? If you were to look at the terms literally, it means the science that goes into studying data is Data Science. The reality is far from this simple explanation. Way back in 2010, Hugh Conway, the labour economics expert of the US, created the Venn diagram that captures the intricate and extensive nature of Data Science. Have a look at it below:
As you can see in the diagram above, there are three major areas or circles that cover Data Science:
- Maths and Statistical Knowledge
- Expansive experience
- Hacking skills
Data Science lies where the three circles overlap and create a confluence. The meeting of the first and third circles of Maths and Statistics and Hacking Skills is the area of Machine Learning. The negativity surrounding hacking has now transformed into ethical and unethical hacking.
A Data Scientist is required to have ethical hacking skills, with extensive experience in mathematical and statistical analysis. While traditional research and Machine Learning are important tools, the probability of a data scientist using their experience to turn from ethical to unethical is high. Learn more about the prerequisite of data science.
What does this denote in the real world?
- You cannot manage, read, or analyze Big Data without the help of Data Science and its branches. Tools, algorithms, principles and applications are used individually or in combination to interpret random data clusters.
- The science requires learning the processes for collecting, preparing, cleansing and analyzing the data.
- As a Data scientist, your job is to extract critical information from a collected set of data applying sentiment analysis, predictive analytics and machine learning.
- The information is then used to guide businesses to create strategies to help marketers and managers achieve organizational goals.
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What does a Data Scientist do?
Businesses expect Data Scientists to solve an issue or provide an answer to a query by following the above-mentioned processes. Once valuable insights are gained, they can use robotic analytics and languages like Java to start creating and exploring programs that will ultimately lead a business to achieve its targets and goals.
Data scientists also employ different methods, like online experiments to ensure sustainable growth for businesses. Additionally, they can also help businesses by developing personalized data products that the companies understand, track and monitor unique patterns, customer requirements and other activities. The ultimate goal is always to help businesses make productive and profitable decisions. Learn more about the job description of data scientists.
What can you expect from the Data Science Learning Path?
A strong career for future scope of Data Science requires you to acquire critical skills in three departments which are programming, analytics and domain knowledge. The learning path will help you acquire the following skills:
- Strong knowledge of Scala, SAS, R and Python
- Understand different analytical functions
- SQL database coding experience
- Working with unstructured data from videos and social media platforms
- Machine learning to make predictive reporting and pattern discovery
- The program will cover inferential and descriptive statistics, natural language processing, model building and fine-tuning
The main focus of data scientists is statistical research and analysis, which is used to choose the right machine learning approach, after which the algorithm is modelled and prototyped for testing.
Using data-oriented technologies like SQL and Hadoop and making extensive use of distributed architecture, data visualization and statistical analysis, Data Scientists extract meaning out of data sets. The learning path will aim at producing skilled professionals who are experts in handling these aspects. They will be trained to switch their roles at any given point as and when required in the lifecycle of a Data Science project.
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What is the structure of the Data Science Learning Path?
Data Science can be understood as the incorporation of different parental disciplines like software engineering, data analytics, data engineering, predictive analytics, machine learning and so on. The learning path should include all of these and a lot more to ensure that you emerge as a skilled Data Scientist. Below is a list to briefly summarize the structure of the Learning Path.
Beginning with Data Science and Python
The journey to becoming a data scientist begins with learning the terms and jargon associated with Data Science. This will involve understanding the role of Data Scientists and getting acquainted with programming languages like Python. Learn more about the languages for data science.
Mathematics and Statistics
This is where you explore the foundation of Data Science. The key concepts that will be covered under this section include probability, basics of linear algebra and inferential statistics. You will also learn how to perform EDA or exploratory data analysis.
Recommendation Systems and Matrix Algebra
You may wonder what Matrix Algebra is doing in the list and why you would require to know it at all. Well, in order to do some serious learning about the working of recommendation engines, matrix algebra is absolutely crucial. This section covers these two trending concepts that need to be understood in relevance with each other. This topic also includes recommendation engine projects and dimensionality reduction techniques like PCA or Principal Component Analysis.
Basics of Machine Learning
This section will introduce you to the basic and core of machine learning. You will learn basic algorithms and techniques which will include logistic and linear regression, SVM or support vector machines, decision trees, Naive Bayes and so on.
With this course, you take a more advanced step into the world of machine learning. The topics here will offer you a clear understanding of what ensembling is along with various ensembling techniques. You will also have to work on data sets to get a hands-on experience of how to solve practical problems.
Deep Learning and Neural Networks
Deep learning constitutes an important section of the Learning Path of Data Science. Considering the astronomical rise in the adoption of deep learning applications, this knowledge is crucial to becoming a skilled data scientist. You will be introduced to Keras, which is a popular framework for deep learning. There are other frameworks like PyTorch that you can choose from as per your preference.
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NLP or Natural Language Processing
NLP is regarded as the hottest field of the industry. Businesses trip over each other to get themselves the best NLP talent. Hence, there was never a better time to engage with NLP. There is a Natural Language Processing frameworks that you will be introduced to in this section. From BERT (Google) to RoBERTa (Facebook), you will learn to work with some of the state-of-the-art frameworks.
This deep learning field is in high demand. In this section, you will deal with a range of problems associated with computer vision and develop practical experience as you proceed.
This is one of the more complex topics in the Learning Path. This topic in itself deserves an entire section which is why you will be made to deal with various hands-on projects to ensure that you understand its practical application. As you get familiar with different concepts in time series, you will also learn their function in the real world.
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The structure of the Data Science Courses designed to facilitate you in becoming a true talent in the field of Data Science, which makes it easier to bag the best employer in the market. Register today to begin your learning path journey with upGrad!
If you are curious to learn about data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
Is it possible to become a data scientist with no experience?
Nowadays, recruiters are more concerned with the skills being possessed by any individual. It is completely possible to become a data scientist even without any experience or master's degree. There are plenty of courses out in the market that can teach you all the necessary skills even if you are not opting for any degree. If you are ready to put in the effort to develop your skills, you can definitely land a job as a data scientist without any experience.
If you follow the below-mentioned steps, you will find it pretty easy to plan your entire career path for becoming a data scientist.
1. Cultivate your math skills
2. Learn certain important programming languages
3. Build up your resume and portfolio with internships and projects
4. Start out with the role of a data analyst
5. Have a valid reason for switching from data analyst to data scientist
You can also explore online learning options that cost far less compared to traditional degrees.
Is it considered to be difficult to land a job in data science?
Getting a job is often a tedious task, and when you are planning to take up a job in any booming field, you need to put in more effort. Data Science is gaining immense popularity in the market, with the relevance of data exponentially increasing for every company. This is why it is pretty difficult to land a job in data science.
It is not always about the applicant being less skilled and not able to land a job. Sometimes, it is a recruiter or the company's problem because they are not clear about the requirements and the skills they are looking for in the employees. If you know the concepts well, you can get a well-paying job pretty easily.
What does an entry-level data scientist do?
Even if you are applying for an entry-level data science job, you need to be familiar with the concepts of probability, statistics, and math. Other than that, you also need to develop a foundational knowledge of programming languages like Python, R, or SQL.
The work of any entry-level data scientist is to collect, manage, and analyze the available data. The main aim of a data scientist is to study the patterns and trends based on the available company data and assess its performance.