Today’s precise and smart technological developments and solutions available in the market in almost every sector are upgrading rapidly; the data is the heart of these upgradations. Various sensors collect data and transfer it to the system. This data goes through multiple processes such as understanding, analysing, concluding, and extracting meaningful information.
These procedures use a scientific approach used, and thus it is known as ‘Data Science’. It is a trending interdisciplinary field of the 21st century. Various scientific methods, algorithms, and unstructured systems extract insights and knowledge from structured and unstructured data. It is closely related to data mining, big data, and machine learning.
The global data science platform’s market size is rising exponentially due to its applications in various fields. The demands for intelligent systems are increasing in multiples with the adoption of advanced technology. The value of data science’s market size was 3.93 billion USD (United States Dollar) in 2019.
It is estimated to expand at a CAGR (Compound Annual Growth Rate) of 26.9% between 2020 and 2027. Rising investments in data science research, development, and technological advances are causing such rapid market growth.
The data science field is exciting and grabbing the attention of professionals and freshers. IT professionals are leaning towards making a career in the evolving data science domain.
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Evolution of Data Science
Data analysis started in the 1960s that has resemblance with data science. The term data science was used first in 1985 in the lecture given at the Chinese Academy of Sciences in Beijing by C. F. Jeff Wu as an alternative word for the statistics. In 1992, three aspects did the successful introduction of a new, interdisciplinary and emerging field of data science:
- Data collection
- Data design
- Data analysis
These theoretical concepts and arguments turned into modern data science in 2001 to expand statistics in technical areas. Though it has been 20 years now, there is no consensus on the definition of data science. It is still a buzzword for many professionals as well as freshers.
Top Data Science Skills to Learn
|SL. No||Top Data Science Skills to Learn|
|1||Data Analysis Programs||Inferential Statistics Programs|
|2||Hypothesis Testing Programs||Logistic Regression Programs|
|3||Linear Regression Programs||Linear Algebra for Analysis Programs|
Exploring Core Data Science Subjects in Detail
As data science expands to achieve an important position in all industries, data science courses will introduce you to the following core subjects that you will be working with as a Data Scientist.
1. Probability and Statistics
When it comes to data science, students need to have a solid foundation in mathematics, especially in the fields of statistics, linear algebra, and probability. All major data science programs include probability and statistics in their course since most machine learning methods, neural networks and other major topics use these skills.
Conditional probability is important for machine learning, while math is required for neural networks. Statistics and probability are also required to work on machine learning algorithms. You should brush up on these skills for an easy understanding of advanced use in data science.
2. Business Intelligence
As a data scientist, you will work with data to help businesses achieve their goals. To ensure profitable decision-making, you need to be up-to-date with the latest business intelligence tools.
The data science course syllabus includes business intelligence because organizations need a professional who can translate the vast amounts of data they have accumulated into simple visual representations to help the business make informed decisions. To become a successful data scientist, you need the perfect mix of critical thinking skills, decision-making abilities and a thorough knowledge of business intelligence tools.
3. Programming Languages
Since you will be dealing with massive amounts of data, you need programming languages to sort, manage and extract valuable information as and when required. When you know the right codes, retrieving the accurate data sets required for analysis becomes easier. Thus, you will be learning various programming languages, including Excel, SQL and Python, which is generally considered to be one of the most efficient programming languages for data science.
4. Machine Learning
Machine learning is an important aspect of the artificial intelligence and data science syllabus that takes the longest to learn. It incorporates a wide range of topics, including the regression approach, decision trees, NLP, text mining and more. You need to be proficient in machine learning to make working with data sets and understanding neural networks much easier.
5. Data Manipulation
Data science is about processing, analyzing and visualizing raw, unstructured data into actionable insights using various tools and techniques. You have to be proficient in visualizing and manipulating data to create data sets that can drive the organization towards streamlining its processes, making informed decisions and achieving its objectives. Thus the data science course syllabus for beginners will create a solid foundation in data manipulation to ensure you can be the driving force for the organization’s success.
Data Science Course Syllabus
In-depth research is improving our understanding and knowledge in Data Science, and thus the study material keeps updating every day for Data Science. There are numerous courses, workshops, training programs, and degrees available for data science held by institutions, universities, and organisations.
With advancements, the data science course syllabus is updated. Some freshers want to start their careers in data science and look for introductory courses that include concepts, hands-on practice, and projects that provide them with the skillset to start working in data science companies.
Most organisations/institutes offer a Data science course syllabus. If we see upGrad’s course syllabus for data science, it includes:
- The concepts of data analysis in excel, Python, and SQL.
- Introductory sessions on Python’s application for Data Science.
- Assignments to strengthen beginners’ ideas. Python is a widely used programming tool for data science and is thus part of all organisations’ data science course syllabus.
- Concepts and hands-on practice on modern technologies such as machine learning, deep learning, natural language processing, computer vision, business intelligence, data analytics, and data engineering.
- Real-time projects for candidates opt to be data scientists, analysts, and developers. These projects help candidates clearly understand technologies and their relevance with data science and finally, how to use them in real-time business development and growth.
upGrad has created one of the most suitable data science course syllabus for professionals. This course is delivered online with learner’s pace and different formats such as certification or Post-Graduate Diploma.
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The course contains preparatory sessions covering data analysis and introduction to the programming language used in data science. Various toolkits like Python, MySQL, and Excel are focused on data toolkits that help candidates visualise, programming, and solve assignments given as a part of the data science course.
If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Program 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.
IT (Information Technology) professionals have experience solving various problems logically and developing the best suitable algorithms. To switch their career into data science, they need to upgrade their analytic skills and apply programming language specifically for data science. There are courses developed particularly for professionals looking to upgrade themselves and their abilities to work on data science projects.
Professionals who are willing to work in data science should focus on upskilling their capabilities and knowledge and looking for a suitable course. Their interest lies in the course syllabus rather than other less relevant aspects of the system. Professionals must choose a data science course that concentrates on data science.
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