Today's organizations struggle with how to make sense of an excessive amount of inconsistent data. A sea of data may be transformed into useful insights that can be used for a variety of purposes, such as recognizing and blocking threats to national security or anticipating the best new diabetic therapy. Because of this, organizations and governmental bodies are scrambling to employ data science specialists who can assist in achieving this goal.

In simple language, a data scientist's job is to study data to find insights that can be put to use.

Particular tasks consist of:

• Identifying the data-analytics Issues That Present The Firm With The Most Opportunities
• Choosing The Appropriate Variables And Data Sets
• Assembling Huge Data Collections, Both Structured And Unstructured, From Many Sources
• Ensuring The Data Is Accurate, Full, And Consistent By Cleaning And Validating It
• Creating And Utilizing Models And Techniques To Mine Large Data Sets
• Data Analysis To Spot Patterns And Trends
• Analyzing The Data To Find Answers And Possibilities
• Presenting Findings In A Graphic Manner and other methods to stakeholders

Common Job Titles for Data Scientists

• Data scientists: Create algorithms, and prediction models, and execute specialized analysis using techniques for data modeling.
• Data analysts manipulate big data sets and utilize them to find patterns and draw conclusions that will help them make smart business decisions.
• Data engineers: Transfer data to data warehouses once it has been cleaned, collected, and organized from various sources.
• Business intelligence specialists should spot trends in data sets.
• Data architects plan, develop, and oversee a company's data architecture.

It is a very rewarding career and if the job roles excite you, joining a course could really help you. There are many data science courses available today. One of the most popular data science course providers is Upgrad.

They have many good features and some of their features are as follows:

• You can prepare for a career in data science and analytics by becoming job-ready. This data science and analytics training course will enable you to draw pertinent conclusions from unstructured data and use those conclusions to guide wise business decisions. In this data science and analytics course for college students, you'll study SQL, ML, and more.
• Learn SQL, Excel, Python, statistics and optimization, predictive modeling, and more as you advance in data science and analytics.
• The best data scientists in the nation instruct students in the best data science and data analysis programs.
• Work on useful tasks to aid in your application of the course material.
• With the help of this curriculum, you can get entry-level jobs in data science and analytics.

However, their inability to provide adequate placement assistance will set them way back in the quality of the course.

Another good option with no notable drawbacks is Learnbay. They offer the best data science courses in Bangalore that can be accessed from anywhere. They have many features that make them the most acceptable course.

• Learnbay offers both online and traditional learning options for its students through the use of hybrid classrooms. They might get the greatest education possible by working on online assignments and learning in virtual classrooms with mentors during live sessions.
• Additionally, the Project Certificate and the Multiple Skills Certificate, both of which are accredited by IBM, are offered by Learnbay's Advanced Data Science and AI Program. The course completion certificate is available in addition to these certificates.
• It's a significant plus that Learnbay focuses primarily on Domain Elective courses. It is one of the most popular and well-liked items on the list. Learnbay's Advance Data Science and AI Program is utilized by numerous different businesses. If you have a strong foundation in critical thinking, you will be better equipped to leverage your industry knowledge and data science skills. Some of the domain electives are as follows:
• Oil, Gas & Energy
• Telecommunication and Mechanical
• manufacturing
• E-commerce and supply chains
• Insurance and finance.
• Hospitality and haulage
• Learnbay offers capstone and real-world projects for students of all skill levels, from beginners to specialists. Throughout the course, you will work on numerous data analytics and data science projects. While you're a student, you can have the opportunity to work in the real world. Since these projects allow students to apply their theoretical knowledge, they are able to move past any lingering obstacles.

A few examples of data science initiatives are listed below:

• Building Chatbots
• Detection of Credit Card Fraud and Fake News
• Recommender Systems for Forest Fire Prediction

• Utilizing Project Innovation Lab's live, interactive learning resources while enjoying the comfort of your own home. Several project innovation centers in at least seven Indian cities, including Delhi, Pune, and others, are helped by experts from MNCs and MAANG. They oversee project sessions both online and off.

I think it is evident that data scientists are very vital for any industry, and hence we can conclude that learning data science if the job roles agree with you could be very rewarding. The job is highly paid and fun for people who are truly interested in the subject matter. In my research, I came to the conclusion that Learnbay offers the best data science course and has exceptional placement support.

Choose to learn and choose your career.

Statistics, mathematics, programming, and domain-specific knowledge are the main expertise that a data scientist must have. Because these work like tools to identify the pattern and present the complex data in an understandable form.

Here are some specific responsibilities that a data scientist carries!📃📃

• Data Collection and Cleaning
• Data Exploration and Visualization
• Statistical Analysis and Modeling
• Feature Engineering
• Model Evaluation and Validation
• Deployment and Monitoring
• Communication and Collaboration

These are the major steps that a data scientist follows to bring the best result to a certain company. Data Scientists are responsible for achieving the desired result. The gathered data are analyzed strategically to find out the hidden patterns which help in predicting future events. 📣📣

However, you will get a clear understanding of practical work. Because data science is a practical subject. You must experience the practical tools and techniques.

But can it be possible?💁‍♂️💁‍♂️

You can consider some courses available online. You have to check the courses if they offer projects or not. Because projects are the best way to learn the tools which a data scientist uses.

I am sharing some courses so that you get an idea. 👇👇

• Udacity’s Data Scientist Nanodegree1️⃣
• Project: Real-time projects like analyzing data from real-world scenarios, building machine learning models, etc.
• Datacamp’s Data Science Track2️⃣
• Project: Interactive projects cover various aspects of data science, including data manipulation, exploratory data analysis, and predictive modelling.
• Learnbay’s Advanced Data Science and AI Program3️⃣
• Project: Simulated real-time and capstone projects like Developing classification techniques, Building recommendation models, and Understanding in-depth logging.

✍Note: If you have a plan to become a data science professional, you can utilize the courses as they offer certification, career services, etc. as well.

Thank You!

Unveiling the Data Scientist: Roles, Responsibilities, and Your Path to Success

Data scientists are the rockstars of the information age! They use their coding skills and statistical knowledge to extract valuable insights from massive datasets. Here's a breakdown:

Top 5 Responsibilities of a Data Scientist:

1. Data Collection and Wrangling: They gather data from various sources, clean it up, and organize it for analysis. Think of it as gathering ingredients and prepping them for a delicious recipe.
2. Data Analysis and Exploration: Using statistical methods and tools, they uncover patterns, trends, and hidden gems within the data. Imagine them analyzing the recipe to understand how different ingredients interact.
3. Model Building and Testing: Data scientists create models that can predict future outcomes or classify data points. Think of them crafting a recipe based on their analysis, then testing it to see if it works!
4. Communication and Visualization: Once they have insights, they translate complex data into clear reports, charts, and dashboards that everyone can understand. Basically, they explain the deliciousness of their recipe to everyone!
5. Collaboration: Data scientists often work with other teams (engineers, business analysts) to translate their findings into real-world solutions. It's like collaborating with chefs and restaurant staff to bring their recipe to life!

Becoming a Data Scientist: Your Learning Journey

There's no one-size-fits-all path, but here are some common routes:

• Get a Degree: A bachelor's degree in statistics, computer science, or related fields is a good foundation.
• Online Courses and Training: Numerous online platforms offer data science courses and programs at various levels.
• Bootcamps: Intensive programs can provide a quick start, but might require a strong foundation in programming and math.
• Self-Learning: Highly motivated individuals can learn through online resources, tutorials, and personal projects.

How Online Courses and Training Institutes Help with Career Opportunities

• Skill Development: Structured courses equip you with the necessary technical and analytical skills.
• Project-based Learning: Applying your knowledge through projects makes you more attractive to employers.
• Career Support Services: Some institutes offer resume writing, interview preparation, and even job placement assistance.
• Industry Recognition: Certain certificates or programs can increase your resume's visibility.

Exploring Top Courses for Aspiring Data Scientists:

• Data Science Certification Course by Simplilearn:
• Benefits: A good starting point to grasp core data science concepts and tools.
• Consider if: You're new to data science and want a foundational course.
• Post Graduate Program in Data Science by Great Learning:
• Benefits: A more comprehensive program potentially leading to a recognized postgraduate qualification.
• Consider if: You have some prior knowledge and want a deeper dive with a potential career boost.
• Advanced-Data Science and AI Program by Learnbay (Best Option):
• Benefits: Focused on advanced topics like machine learning and artificial intelligence, potentially good for experienced professionals.
• Consider if: You have a strong foundation in data science and want to specialize in AI.

Remember: The best course depends on your background, goals, and learning style. Research course content, instructor experience, and career support options before enrolling.

Bonus Tip: Building a strong portfolio showcasing your data science projects can significantly enhance your job prospects.

Thank you

What is Data Science?

I think this van-diagram is pretty self explanatory.

What does a Data Scientist do?

Tries to make predictions, using statistics and machine learning. Data scientists need to know how to deal with large amounts of data, a.k.a big data. There are great books that deal with predictive analytics and big data (the ones I read):

- Big Data: A Revolution That Will Transform How We Live, Work, and Think: Viktor Mayer-Schönberger, Kenneth Cukier: 9780544002692: Books
- Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die: Eric Siegel, Thomas H. Davenport: 9781118356852: Books

An example to what you can do with big data:
1. Dr. Kira Radinsky from Technion Institute in Israel predicted riots and disease outbreaks (see video below).
2. The infamous Nate Silver (FiveThirtyEight), that predicted elections.
3. Facebook, Google, Amazon, and many other companies use their large volume of data to predict things you will like and suggest them to you.
4. Kaggle: Go from Big Data to Big Analytics, companies post problems and you can compete to solve them... and win money :)

How to become one?

That's a bit trickier to answer, since it is a relatively new field and the paths vary. There are a bunch of programs that have opened up all across the US, Europe and Canada. You can easily Google them. But, how valid will they be against a Math, Statistics, or a CS degree is still unclear.
Most of the people that are working right now as data scientist got there by studying math, statistics or programming, which are the core skills you need to become one. So, I'd say you have three options, and which is the best only time will tell.
1. Study math/statistics/programming
2. Study data science programs
3. Study on your own through MOOCs there are a bunch of them that can get you started. My favourite is: Coursera and CS109 Data Science. They assume that you know programming already, but you can definitely follow the course lectures and get a good understanding of the subject without it. Though you should definitely get a good grasp on python in the near future.

After you feel a bit more comfortable with the subject you should try Kaggle competitions, or you can make up your own questions/projects and solve them. You'll need to show employers what you can do. I know a few people that got positions this way, they studied on their own by focusing on completing a project, and then used that project to show-off their skills during an interview.

Hey all! 🧑‍🎓

The basic job responsibilities of a data scientist are listed below for your reference:

1. Identifying valuable data sources or automating collection processes. 💻
2. Undertaking preprocessing of structured data and unstructured data.
3. Analyzing large amounts of data to discover patterns.
4. Building predictive models as well as machine learning algorithms.

These are some of the responsibilities a data scientist deals with in their career, do not be confused because there are many. Once you register your career in data science, you will find a lot of other responsibilities as well. 🙌 ✨

But, regardless of what responsibilities of a data scientist, you need to have some important skills and knowledge to excel at your work. 📈

Let’s know how.

Learnbay- Here you will find the professional program named Advance Data Science and AI Program with domain specialization. 🔥

DataCamp: This institute is quite popular for providing different kinds of data science programs - Data Science and AI.

Emerticus: The institute offers a wide range of data science programs but what I recommend you here is the Data Science Certificate Course. 🧑‍🎓

Note: These programs are offered online so you will find many advantages, but the most I found is from Learnbay where they have simulated project training. 🙌

On a last note, data science requires knowledge of programming tools, SQL, and others. Keeping that in mind, I have listed some major responsibilities for people working as a data scientist.

Hope it helps! 👍

Hello Guys,

A data scientist plays a crucial role in extracting valuable insights from data to inform decision-making and drive business outcomes.

Together let’s witness some key responsibilities and tasks that a data scientist typically performs:

• 🟠 Data Collection and Preparation: Data scientists gather data from various sources, including databases, APIs, sensors, logs, and external sources.
• 🟠 Exploratory Data Analysis (EDA): Data scientists conduct exploratory data analysis to understand data distributions, correlations, trends, and patterns.
• 🟠 Statistical Analysis: Data scientists apply statistical techniques and hypothesis testing to analyze data, test hypotheses, make predictions, and draw meaningful conclusions.
• 🟠 Machine Learning Modeling: Data scientists develop and implement machine learning models to solve predictive analytics, classification, clustering, and recommendation problems.
• 🟠 Deep Learning and Neural Networks: In cases where complex patterns or unstructured data are involved, data scientists may work with deep learning techniques and neural networks.
• 🟠 Data Visualization and Reporting: Data scientists create visualizations, dashboards, and reports to communicate insights and findings effectively to stakeholders. They use tools like Tableau, Power BI, Matplotlib, Seaborn, or ggplot2 to visualize data, trends, and key performance indicators (KPIs).

👉👉Above-stated were a few roles and responsibilities that are done by the data scientist but what needs to be done to become a data scientist.

Confused right?😕

So to become a professional data scientist you must take up some online courses from a good institute where you will be guided and trained in the domain in a detailed manner and land yourself in your dream role in a dream company and position.

A few examples that you might consider are as follows:

👉 Learnbay

Course: Master’s in Computer Science: Data Science and AI

Key Skills:

1. The course offers domain-specific knowledge where some of the domains that are included in the learning process are BFSI, Healthcare, Supply chain, etc.
2. Globally accredited certifications from IBM and Microsoft
3. Career Pro Service where enrolled students will get services like resume building, 1:1 mentorship, unlimited interview calls, etc.

👉 Analytix Labs

Course: Data Science 360 Course

Key Skills:

1. Offers a comprehensive curriculum where all the necessary skills and concepts are been included for better learning and understanding.
2. Opportunities to work on real-time and capstone projects
3. Career support is offered to each student where they get services like profile building, interview preparations, job referrals, etc.

Ending Statement:

Data scientists leverage their expertise in data analysis, statistical modeling, machine learning, and domain knowledge to extract actionable insights, solve complex problems, and unlock value from data for organizations across various industries.

All the Best!

During my MBA at one of the leading B schools in India, market research, business intelligence and analytics were my favorite courses which motivated me to learn Data Science in depth during my free times. As per my understanding, a data scientist is someone who knows to extract raw data, interpret hidden factors and convert them to actionable insights for a business. Most of the data scientists spend a lot of time in collecting, cleaning and munging data because data is never clean. However they miss out to concentrate on the big picture such as deriving useful actionable models without affecting the value chain of the business. This is because data scientists in India are mostly statisticians, programmers and engineering graduates who are molded by the business to perform data analytics whereas they are not strong from a business domain perspective. Ideally, a data scientist is someone who has 4 important skills namely statistical skills, programming skills, business domain based skills and interpersonal skills namely presentation and business communication.

No one knows. Sometimes it’s a machine learning researcher. Sometimes it’s a statistician. Sometimes it’s a non-technical business analyst who knows how to work Excel. Sometimes it’s a programmer with a background in a scripting language and a knack for writing clever database queries. Sometimes it’s some guy who took an undergrad stats course once and recently passed a free online course.

“Data scientist” can mean many different things, to the point where it more conveys how someone wants to be viewed than it does their actual skills and experience. It’s as much a buzzword as it is a job description, and though in the context of a particular workplace it might have a well defined meaning, it certainly doesn’t in the industry as a whole.

Howdy! 🤠🙌

Ah, data science, the magical realm where numbers dance and insights abound! 📊✨ Let me break it down for you!

What does a data scientist do?

🤔Picture this: You're a data scientist, a modern-day Sherlock Holmes 🕵️‍♂️ armed with algorithms and Python code instead of a magnifying glass and a deerstalker hat. Your mission? To extract valuable insights from heaps of data! 📈

Here's the scoop:

1️⃣Data Wrangling: You roll up your sleeves and dive into messy data like a pro cleaner organizing chaos. 💼🔍

2️⃣Analysis: Armed with statistical techniques and machine learning models, you uncover hidden patterns and trends, revealing secrets that data holds. 🔍🔮

3️⃣Visualization: You're not just about crunching numbers; you're also an artist, crafting captivating visualizations that tell stories and captivate audiences. 🎨📊

4️⃣Communication: Your findings are gold, but they're meaningless if no one understands them! You translate complex analyses into plain English, making data-driven decisions a breeze. 💬💡

5️⃣Continuous Learning: In this ever-evolving field, you're a perpetual student, constantly updating your skills and staying ahead of the curve. 📚🚀

Where can you learn?

Online platforms like Edx, Intellipath, and Learnbay are your virtual playgrounds, offering a plethora of courses tailored to budding data scientists:

📍Edx: Dive into courses like "IBM: Introduction to Data Science" to master the art of data wrangling, analysis, and visualization.

📍Intellipaat: Explore their "Data Science Course" track, where you'll learn everything from Python programming to machine learning algorithms.

📍Learnbay: Embark on their "Advance Data Science & AI Certification Program for Professionals," a comprehensive journey through data analysis, visualization, and predictive modeling. (Recommended

So, if you're ready to embark on a thrilling adventure into the world of data, buckle up and get ready to unlock insights that can change the game! 🚀🔓

Happy Learning! 🤠💫✨

A data scientist represents an evolution from the business or data analyst role. The formal training is similar, with a solid foundation typically in computer science and applications, modeling, statistics, analytic and math. What sets the data scientist apart is strong business acumen, coupled with the ability to communicate findings to both business and IT leaders in a way that can influence how an organization approaches a business challenge. Good data scientists will not just address business problems, they will pick the right problems that have the most value to the organization.

In laymen terms , someone who can represent loads of data using mathematical tools , say bar graph and stuff !

Data scientists work closely with business stakeholders to understand their goals and determine how data can be used to achieve those goals. They design data modeling processes, create algorithms and predictive models to extract the data the business needs, then help analyze the data and share insights with peers. While each project is different, the process for analysing the data generally follows the below path:

1. Ask the right questions to begin the discovery process.
2. Acquire data.
3. Process and clean the data.
4. Integrate and store data.
5. Initial data investigation and exploratory data analysis.
6. Choose one or more potential models and algorithms
7. Apply data science methods and techniques, such as machine learning, statistical modeling, and artificial intelligence.
8. Measure and improve results.
9. Present final results to stakeholders.
10. Make adjustments based on feedback.
11. Repeat the process to solve a new problem.

Most data scientists use the following core skills in their daily work:

• Statistical analysis: Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.
• Machine learning: Implement algorithms and statistical models to enable a computer to automatically learn from data.
• Computer science: Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
• Programming: Write computer programs and analyze large datasets to uncover answers to complex problems. Data scientists need to be comfortable writing code working in a variety of languages such as Java, R, Python, and SQL.
• Data Storutelling: Communicate actionable insights, often for a non-technical audience.

Data scientists play a key role in helping organizations make sound decisions. As such, they need “soft skills” in the following areas.

• Business intuition: Connect with stakeholders to gain a full understanding of the problems they’re looking to solve.
• Analytical thinking. Find analytical solutions to abstract business issues.
• Critical thinking: Apply objective analysis of facts before coming to a conclusion.
• Inquisitiveness: Look beyond what’s on the surface to discover patterns and solutions within the data.
• Interpersonal skills: Communicate across a diverse audience across all levels of an organization.

Asked so many times by so many people in the form of so many duplicate questions on Quora itself so kindly browse cautiously. I'll describe it once again.

Broad level skills required

Details of skills required

The role of a data scientist focuses on extracting actionable insights from data through statistical analysis, programming, and communication.

Here’s a concise overview:

1. Data Collection and Preparation

- Data Gathering: Collecting data from databases, APIs, or third-party sources.

- Data Cleaning: Addressing missing values and removing duplicates.

- Data Transformation: Structuring data for analysis.

2. Data Exploration and Analysis

- Exploratory Data Analysis (EDA): Using visualizations to identify patterns and trends.

- Statistical Analysis: Testing hypotheses and validating findings.

3. Building Models

- Machine Learning (ML): Creating algorithms for predictions and classifications.

- Deep Learning (DL): Utilizing neural networks for complex tasks like image recognition.

- Optimization: Enhancing model performance.

4. Interpreting Results

- Data Storytelling: Communicating findings effectively to stakeholders.

- Insights: Providing recommendations for decision-making.

5. Deployment and Monitoring

- Integration: Implementing models into production.

- Monitoring: Tracking model performance and retraining as required.

6. Collaboration

- Teamwork: Collaborating with analysts, engineers, and other teams.

- Domain Knowledge: Understanding the business context for impactful solutions.

7. Staying Updated

- Continuous Learning: Keeping up with data science advancements.

- Research: Exploring new techniques for improvements.

Key Skills and Tools

- Programming: Python, R, SQL

- Visualization: Tableau, Power BI

- ML Frameworks: Scikit-learn, TensorFlow

- Big Data: Hadoop, Spark

- Cloud: AWS, Google Cloud, Azure

This is a FANTASTIC question. Specifically the first part. What is a data scientist? I’ve read hundreds of responses to similar questions all over the web, many of them right here on Quora.

I hate to be the one to tell you, but nobody knows the answer to this question. The reason for this is that the job description from any two companies can be vastly different. I’ve seen descriptions that included the following qualifications (give or take):

• Company A - Seniority Level: Management. Minimum education of a MS in STEM (PhD highly preferred) and 5 years of related industry experience in a data science / data analysis role. Must be familiar with toolkits such as Python, R, Tensor Flow, NLP, H2O, Keras and proficient with command line usage. Be able to answer questions directly related to the business using advanced analytic techniques.
• Company B - Seniority Level: Entry Leve. Minimum education of a BS in STEM and 1 year of industry experience in a data analyst role. Must have SQL and Excel experience. Be able to provide insight with data.

Clearly, these are two VERY different jobs and should have different job titles. So which is the sexiest job of the 21st century? It used to be Company A’s definition (I think) but since the hype took over, Company B is becoming more prevalent.

What are the bases to acquire to become a data scientist? Depends on which kind of data scientist you want to be. Company A or a Company B. I can tell you one thing. They aren’t the same job. Not according to duties, seniority, responsibility or pay.

Thanks for A2A.

I have had a chance to train / work with, some of the budding data scientists from various companies. Let me share my experience here:
Firstly, no one (yes, no one), really knows what is the right definition of a data scientist. This means, people have are not sure what to expect from training sessions. So more often than not, I cover the basics, which include problem-solution modeling, basic statistical techniques, data collection and cleaning, some programming, and tools. In essence a data scientist is expected to work on specific domain, and convert information to knowledge and use this knowledge to come up with insights for better decision making. The nitty-gritty level details will vary from organization to organization.

The field of data science is attracting a diverse range of individuals who are drawn to the exciting intersection of technology, statistics, and problem-solving. Whether you're a curious mind eager to uncover patterns in data, a tech enthusiast intrigued by the potential of machine learning, or someone passionate about leveraging data for informed decision-making, the role of a data scientist offers a compelling journey. If you're considering a career in data science or simply want to explore the fascinating world of data, visit and follow my Quora profile for valuable insights and discussions on this dynamic field.