Before defining the importance of programming language in data science, it’s important to understand the meaning of programming language and data science.
Programming Language is a set of instructions or commands used to either write code or to create software programs. In simple words, a programming language is a language that gives commands to computers to perform certain tasks. It can be in the form of command, code, syntax etc. It is broadly classified into 3 types:
• Machine Level Language
• Assembly Level Language
• High Level Language
A programming language is an artificial language that is used to control computer systems. There are various types of programming languages like Python, C, C++, Java, JavaScript, R etc.
Data Science: Data science is a field of study that deals with data. Data Science extracts meaningful results from unstructured data. It combines domain expertise, programming, knowledge of mathematics & statistics, algorithms, data structure etc.
Data is multiplying everyday, which has become a challenge for companies to manage huge amounts of data collected from ample sources and convert it to assets that empower better business decision making. So to understand and manage data effectively, both programming language and data science are essential elements. As data science converts raw data into useful information easily accessible by all stakeholders for implementing better decisions. To convert data, certain steps are followed by the computer system. To give instructions to a computer, a programming language is required.
People who perform all functions like analyzing, interpreting, organizing etc related to data are called Data Scientists, Data Analyst, Business Analyst, Web Developer etc. Data Science has become one of the most highly paid careers, with passage of time. To pursue career in data science, you should acquire knowledge of its important concepts like:
• Data Structures & Algorithms
• Machine Learning
• Coding
• Mathematics and Statistics Computation
• Business Intelligence
Four Components of Data Science:
1. Data Strategy: Data Strategy means determining what data needs to be collected and why? In other words, Data Strategy makes connections between data that need to be collected and business goals. Every data collected can’t be useful, so strategy should be to gather data which fulfill business goals. It is important to explicate business goals and data gathering.
2. Data Engineering: Data Engineer transforms data into useful format for analysis. Data Engineering is the practice to design and build systems for collecting, storing, analyzing data. It is a complex task of converting raw data into usable information for data scientists and other employees within the organization.
3. Data Analysis and Models: To extract insights or make predictions, data analysis and mathematical models play a vital role. To determine predictions algorithms, computations, statistics analysis etc. are required. Data models organize data elements and regulate how data elements are related to each other.
4. Visualization and Operationalization: Presenting data is not sufficient to solve business problems, but to bring the right data, at the right time, for the right users. So, both data visualization and data operationalization plays a crucial role in handling data. Visualization is broader in term; it is not limited to presenting data but includes looking at raw data again, and presenting it in such a way to convert it into useful information.
Along with above mentioned concepts, it’s important to know about the data science process. What steps are followed to solve problems in data science?
Data Science Process: To understand what data scientists do, let’s understand the data science process:
• Frame the problem and setting the research goal
• Collect raw data: Internal data and External data
• Process the data for analysis: Data Cleaning, Data Transforming, Combining Data
• Data Exploration (Explore the data)
• Data Modeling (Perform analysis)
• Communicate results: Presentation and Automation
Programming Language is crucial for all directions in data science. Languages like Python, R etc act as a base for data science and analytics, others like C, C++, Java, and JavaScript etc are useful for data systems development. Data Science requires programming languages to help information extract the value of data.
All programming languages are important in data science, depending on the domain and industry. Most commonly used languages are Python, R, SQL and Java etc.

Data Scientist is required in every job sector. People from any background can learn data science, even if you are from a non-technical stream. You can choose a data science career by gaining knowledge from certified courses. There are ample resources through which any aspirants can master data science skills, available on the internet. You can choose any source which is suitable as per your requirement. Here are few best data science institutes, which offer best data science course:
1. Upgrad: Upgrad offers a professional certified program to learn problem solving skills in data science. Upgrad courses are designed mainly for technical background people. You can attain knowledge of machine learning, data engineering, business analytics, natural language processing etc. You can choose any program as per your goal, Upgrad provides a degree like Master in Data Science, Executive PG program etc.

  1. Coursera: Courses are designed very well as per your level for both beginner as well as intermediate. You can learn courses from the beginning, you don’t need prior experience. You get exposure to real life industry projects and can learn Python, SQL, Cloud databases, Machine Learning, Algorithms etc. Fresher can also opt for these courses but if you are looking for advancement in career, will suggest opting for its alternatives.
  2. Simplilearn : It is one of the best platforms to gain proficiency in data science and programming language. Courses are well structured; fulfill all requirements to become a data scientist. Simplilearn offers 10+ courses in data science from micro degree to PGP programs etc, in this you get access to online boot camp, Machine Learning, Python, SQL, and Big Data etc. Although sessions conducted are interactive mentors but batches are huge, which make it difficult to accessibility to clear doubts.
  3. Learnbay: It is one of the best institutes in data science, which also provides domain specialization. Learnbay offers various courses as per industry’s need. You can learn Domain training, Google cloud, Deep Learning, Power BI, Computer Vision, AI/ML project management, Machine learning, NLP, Python, SQL, R programming etc. Learnbay data science certification courses are designed for all working professionals from any stream, as you can start from scratch. By opting learnbay, you can specialize in data science in one or multiple domains like HR, Finance, Marketing, Telecom, Banking etc., To become data scientist, you should be specialized in one domain. You get experience of real life projects, lifetime access etc.

    Apart from above mention courses or institutes of data science, you can also gain programming skills and data science knowledge by YouTube Videos, Books, etc, but as to become master of any skills, both practical and theoretical aspects are required, which can be gain from such platform where you are taught all important concepts and get exposure of real life industry projects. So, Courses, programs, or institutes are one of the best sources to attain fully fledged knowledge with expert understanding of practical application.
    Lastly, I would like to add that Data Science is the study of data. Data has become an expensive asset which is difficult to manage nowadays if you don’t have proper skills to convert chaos of data. To handle data, you should attain skills to represent it into useful information, for which programming language is the base. To learn programming skills and managing data, you are required to attain certified knowledge. For this, you can opt any institute as per your requirement or any source whatever is suitable. All sources are a good mode of imparting education but it depends on individual learning capability and goals. So, choose as per your own suitability, it’s not necessary that if one course is good for an individual, it will be good for other individuals also. As data science suggests to produce useful outcomes from a plethora of information, the same way, you can analyze each source and can initiate your learning by choosing the right source.

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