Data Science in Hindi
Learn Data Science, EDA and Machine Learning in detail through this Data Science in Hindi Course
Instructor:
Mr. Bharani AkellaSkills you’ll Learn
About this Free Certificate Course
Data science is a protean field that uses scientific processes, methods, algorithms, and systems to extract multiple insights and knowledge from data in various structured and unstructured forms, similar to data mining. A data scientist can understand data, identify patterns, and make predictions using data. Data science is a process that includes data collection, data cleaning, data analysis, and data visualization. Hence, to become a successful data scientist, one must be thorough with their Data Science basics.
In this free Data Science in Hindi course, you will go through a brief introduction to Data Science, where you will understand Data Science and the role it plays in the real world scenario. We are well aware that humongous data is produced daily, and to sort these data, you need specific skills which you will gain by learning about Data Science. This free course will explain to you the Data Science lifecycle in detail through which you will understand how data is processed. Along with Data Science, Machine Learning also holds a prominent role in handling data. Thus, the course explains the Machine Learning basics in-depth. You will go through multiple case studies to understand Data Science better and learn to apply it to real-world problems. Enroll in this informative Data Science in Hindi course and gain a free certificate.
You can satisfy your need to understand Data Science deeply by going through Great Learning’s Best Data Science Courses. Enroll in the top-rated Degree and PG programs that support your career path and earn a certificate of course completion that increases the value of your resume.
Course Outline
This section gives you various examples to help you understand Data Science. It explains how you decide on a place for the vacation, how the weather is predicted, and sales during a particular time in a year using data science.
Data Science life cycle revolves around data acquisition, preprocessing, ML algorithms, pattern evaluation, knowledge representation, and analytical strategies to predict and proffer insights, which we shall learn in this module, into the procedures to yield the best results.
In this module, we will discuss about the machine learning basics in detail.
In this module, there will be an in-depth discussion of case studies on data science.
Our course instructor
Mr. Bharani Akella
Data Scientist
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Frequently Asked Questions
What is data science course eligibility?
There is no hard and fast eligibility except for the learners to graduate in a STEM or any other domain.
What are the types of data science?
Essentially data science based on the type of data could be qualitative or quantitative. Then there are various domains of data science such as data mining, data visualisation, database management and architecture, data analytics in operations, and many more.
What does a data scientist do?
A data scientist performs a wide range of activities and carries various responsibilities such as:
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Identify important questions
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Collect relevant data from relevant sources
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Store the data and organise it
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Draw useful information and insights from organised data
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Translate the information into business solutions
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Communicate the findings that affect the business positively
What is an example of data science?
Some of the prevalent examples of Data Science are personalised healthcare recommendations, identifying and predicting a disease, optimising shipping routes in real-time, and other similar applications.
Does Data Science Require Coding?
Yes, knowledge of programming languages and coding is essential to become a successful data science professional.
Popular Upskilling Programs
Data Science in Hindi
Data Science is one of the most sought after career choice among professionals looking for a bright career. It is also highly sought after by organisations that focus on collecting data and drawing meaningful insights for better decision making. Data is a great asset to an organisation but only if it is processed and analysed properly. Also, the data storage needs of organisations have grown multifold in this age of big data. With frameworks like Hadoop, the storage part of the data has been taken care of. Until 2010, a major focus had been towards building a state of the art infrastructure to store organisations’ important data. The focus now is on processing the data.
Importance of Data Science for Businesses
From working with small and structured data sets to working on large data mines of unstructured and semi-structured data that comes in from different sources, we have come a long way. The traditional business intelligence tools are not capable of processing the massive pool of unstructured data. Therefore, Data Science includes more tools to work on large pools of data collected from different sources. Businesses use data science to find patterns and valuable information within data such as financial logs, media files, marketing forms, text files, values from sensors and instruments, and much more. There are various use cases that have made data science popular among organisations, some of these are:
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Predictive Analytics - Taking an example of weather forecasting, the data is collected from radars, ships, satellites, and aircraft for building models that forecast weather and predict natural calamities with precision.
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Product Recommendations - E-Commerce and retail businesses are recommending products with more precision than ever. They do it by drawing insights from browsing history, demographic factors, and purchase history. Large volumes of data train the models better to come with more precise recommendations.
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Effective Decision Making - Self-driving cars are the best example of this use-case. An intelligent vehicle works by collecting data real-time from surroundings with the help of sensors such as cameras, radars, and lasers creating a visual map of their surroundings. The advanced machine learning algorithms use this data to train for making crucial decisions such as stopping, speeding, and turning.
Role of a Data Scientist
The role of a data scientist is interesting as it cumulates a wide range of tasks and responsibilities. Essentially, a data scientist will:
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Identify important questions
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Collect relevant data from relevant sources
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Store the data and organise it
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Draw useful information and insights from organised data
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Translate the information into business solutions
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Communicate the findings that affect the business positively
Building complex quantitative models and synthesising large volume of data are not the only KRAs that data scientists need to master. They should also have excellent communication and leadership skills. It is essential to drive measurable and tangible results to various business stakeholders.
Important Skills to Become a Data Scientist
The field of data science includes concepts from various disciplines and hence demands a range of skills such as mathematical expertise, technology skills, and strong business acumen. Some of the top skills required to become a data scientist are:
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Mathematical Expertise: When we talk about mathematics in data science, many believe that data analytics is all about statistics which is a misconception. Both classical and Bayesian statistics are very crucial to data science, but other concepts such as quantitative techniques and linear algebra are also crucial for data science. These techniques are a support system for many inferential tasks as well as machine learning algorithms for data science applications.
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Business Acumen: Data Scientists derive useful information that is crucial for businesses. They are also responsible for sharing this information with concerned stakeholders and teams so that they can apply it to the business processes and solutions. They are likely to contribute to business strategies as they are exposed to the data and the information they derive from it. Therefore, a data scientist will require to have a strong business acumen so that they are able to fulfil their responsibilities.
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Technology Skills: Data Scientists need to work with complex algorithms and sophisticated technology tools. They are also expected to code and prototype solutions with less turnaround time using programming languages such as Python, R, SQL, Java, SAS, Scala, Julia and many others. Also, to surf their way through technical challenges that might arise, they need to have a fair technical understanding of systems, software, snd networks that they use. This helps them avoid any bottlenecks or roadblocks that arise due to lack of technical soundness
The top five soft skills that recruiters seek in candidates interviewing for the position of a data scientist are:
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Statistical Thinking
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Technical Acumen
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Multi-modal communication skills
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Curious minD
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Creativity
About The Program
The program will provide you with a comprehensive introduction to the field of data science. You would learn the fundamentals of data science and data science lifecycle. The course has case studies in Python that will help you understand how to use libraries such as Matplotlib, Numpy, and Pandas for the purpose of exploratory data analysis. With this course, you will learn data science with Python.
Most of the courses for modern technology subjects like data science, machine learning, cloud computing, artificial intelligence and others are delivered and taught in English. Some of us might not be really comfortable with the English language and hence miss out on the opportunity to learn the subject and work in the field of data science. Therefore, Great Learning Academy gives an option to learn data science in Hindi for those who are more comfortable with Hindi as a mode of teaching and not English.
It is a data science online free course of two hours duration in the form of video content. You also get to appear for a quiz to measure your learning from the course and improve with practice. Upon successfully completing the course you will get a data science certification of completion from Great Learning which will add value to your profile as a data science professional.