Overview of the Data Science Online Course 

The Data Science course curriculum includes instruction in a variety of subjects such as mathematics, statistics, programming, data visualization, and so on. Let us look into each of these in detail.

Mathematics

The DS models and tools mostly work with simple mathematical formulas. It is thus necessary to learn mathematics to work efficiently in the field of data science.

Programming

Knowledge of programming languages is a must for data scientists. They must be well versed in coding algorithms and work with databases. The data science curriculum thus emphasises laying a strong foundation in programming languages such as Java, Python, C, and C++ and their usage in business.

Data Visualization

It is one of the most important topics included in the DS curriculum as it helps learners understand the ways of representing data in a graphical form, including pie charts, diagrams, and images, to present reports on analytical concepts.

Machine Learning

It is a part of data science that helps make machines intelligent and smart. This enables machines to perform functions similar to those of the human brain. It includes the study of technology, machine learning systems, and other modern technologies.

Probability and Statistics

Probability and Statistics techniques help to perform data segregation with ease.

Data Science Process

Data science is applied to data and aids in the extraction of information by following a process. The five important stages of data processing are:

Collect: The first step in the process is data acquisition and extraction. It includes data entry, signal reception, and gathering raw, structured, as well as unstructured data.

Manage: This step involves Data Cleansing, Processing, Warehousing and Architecting. It involves taking raw data and putting it in a systematic form.

Process: Processing data includes methods such as mining, modelling, classifying, and summarizing. The scientists take prepared data and study the range, patterns, categories, etc. before determining its usage in predictive analytics.

Analyze: The data is analysed using various exploratory, confirmatory, predictive, regressive, and qualitative methods. This is the principal step of the data processing cycle.

Report: The final step of data processing is finalising the report that is created to facilitate the extraction of information required for business decision-making. The reports are usually prepared in an easy-to-read format with the use of charts, graphs, and diagrams.

Who are data scientists?

Individuals who are experts in data management flow are known as "data scientists." They collect, manage, analyse, process, and interpret data to draw inferences. They make use of various tools and techniques to handle complex issues and solve them. The DS professionals are a mix of mathematicians, computer science experts who must also possess programming knowledge. They are in high demand in both the business and IT sectors.

The key responsibilities of a data scientist are:

• Collect structured and unstructured data and maintain it.
• Improve data quality by employing machine learning techniques.
• Identify trends and patterns using different data analytics and algorithms.
• Implement a data-driven approach to perform automation and solve complex problems.
• Perform data cleansing, segregation, and interpretation.
• Share useful insights with the process's stakeholders.
• Prepare reports after processing the data and communicate them with the management team.

Roles available in the field of Data Science 

Data Science is a field where an individual can find many job opportunities as per their interests. Following course completion, a Data Science learner can take on the following roles.

Data Analyst

The main role of a Data Analyst is to fill the gap between business analysts and data scientists. They use technical analysis to turn data into qualitative information used for taking action. Data analysts must possess some key skills, such as mathematical & statistical skills, programming skills, and data visualisation & wrangling.

Data Scientist

A Data Scientist is responsible for finding answers to complex questions through data processing. They are responsible for extracting, cleansing, and preparing the significant data. They require skills like storytelling, data visualization, statistics, mathematical ability, programming skills, Hadoop, SQL, and Machine Learning.

Data Engineer

Data Engineers are responsible for organizing, developing, managing, deploying, and optimising organisational data. The data engineers help the data scientists with transferring and transforming data for handling queries. Data Engineers must have an understanding of databases, different frameworks, and programming languages.

Data Science Expert-Skills

A data science expert should be proficient in data handling, which calls for a set of skills that they must possess to handle data effectively. The eligibility criteria for the data science certification course is that the aspirants must be graduates from any stream who have secured an aggregate score of 50% or more. It is widely assumed that individuals with backgrounds in engineering, information technology, marketing, and other related fields are well-suited for data science positions. It can also be pursued by working professionals who have completed their BBA or MBA.

What are the tools used by Data Experts?

There are various tools useful for Data professionals required to combat the challenges faced by them in handling the data, few of them are R Studio, SAS, RapidMiner, AWS Redshift, Excel, MATLAB, Jupyter, Informatica, Tableau, RAW, Cognos, SPARK, Azure ML, etc.

Advantages of Obtaining a Data Science Certification 

Data Science Certification can be helpful in getting a suitable job in this domain. It is nowadays required in various fields, and hence the demand for data scientists is rising day by day. Given below are some of the fields in which the use of DS is pertinent.

Logistics

Logistics companies use DS to determine the best route for faster delivery of goods. It is thus useful in enhancing their operational efficiency.

Airline Routing

DS is used in optimising the flight routes and in the prediction of possible delays. It is also helpful in making an emergency landing and spotting a suitable place for one in case one is required.

Gaming

The game developers also make use of the DS to program the video games, which has enhanced the gamer’s experience.

Medical Care

The companies that are involved in making medical instruments with accuracy use data science. They also make use of it to detect and cure diseases.

Internet Surfing

The internet surfers use words to search for the topics that they are looking for, and the search engines make use of data science to pick the most relevant headings available on the internet to fetch the required information.

Finance

Data science has proven useful in banks and other financial institutions. They use it to extract data and analyse it. This helps them predict financial risks and manage them.

Manufacturing

DS applications are used in manufacturing, distribution, and managing the supply chain. It is helpful in discovering and maintaining the faults in the equipment.

Automated Technology

Advanced and automatic technology is making use of DS concepts for innovative inventions. The automated vehicles use sensor technology. To make the sensors more effective and accurate, the companies are making use of DS.

Entertainment Industry

DS is also used in the entertainment industry and helps provide streaming services to customers. Algorithms are employed to make use of data that aids in the creation of a suggestion list based on user preferences.

Image Recognition

An important application of Data Science is that it is helpful in the identification of patterns in images and helps to detect objects.

Where can I get a data science course?

Talentedge, in collaboration with an esteemed and prestigious institute, provides an online platform to pursue the best data science courses. A few institutes through which learners can complete a certification course in DS from India and abroad are Liverpool Business School, IIIT Bangalore, the University of Maryland, and XLRI Jamshedpur. The fees for data science courses vary by institute, but they typically range between Rs. 4.2 lakh and Rs. 1 lakh.

Participants can enrol themselves in one of these best data science courses online and earn a high salary package in the initial stages. The salary will increase with time and experience. Given below is the salary breakdown of Data Science professionals on the basis of years of experience.

Thus, it is a good field in which learners can build a career if they have an interest in statistics, mathematics, data handling, and management. It has a wide scope in the current business and IT sectors, and the field is growing day by day.

FAQs

1. What is the differentiating factor between Data Science, Machine Learning and Artificial Intelligence?
   Artificial Intelligence works on the concept that computers can work smartly like humans. Data Science is one of the subsets of AI that deals mainly with statistics, data, and analysis used to gain meaningful insights. Machine learning works on the concept of teaching machines with past data to enable them to make human-like decisions at their own discretion.
    
2. What should I know before taking a Data Science Course?
   A person enrolling in a data science course needs to have knowledge of various programming languages such as C/C++, Java, SQL, and Python, as coding is commonly required in the data science roles to form structured data sets.
    
3. What are the three main concepts of Data Science?
   The three main concepts of Data Science are data design, collection, and analysis.
    
4. What is the best specialisation course for data science?
   Data Science is a top-rated program, however, a specialisation in Machine Learning and Algorithm Development is highly valued as it can help develop algorithms and AI-based solutions.
    
5. What are the different types of data science?
   Data Science has found a place in all industries, and the top roles include data mining, machine learning, predictive modeling, data analysis, and business analysis.