What is the best online data science program to start a data science career?

Updated on : December 3, 2021 by Anthony Lloyd



What is the best online data science program to start a data science career?

Coursera Data Science and Machine Learning Specialization Course. In addition to the kaggle competition. For a deep understanding of all models, use Analytics Vidhya. The more you practice, the more you will learn. Note that it is an ocean. Until you have passion to solve the problem, you will not be able to swim in this

Good question! Data Science and Big Data are probably the most popular terms used in the tech industry right now.

Let's first understand, what is Big Data?

Big Data is the collection of data that you cannot store or process using the traditional database system within the given time frame. There are many misconceptions when referring to the term big data, we use the term big data to refer to data that is in gigabytes or terabytes or petabytes or whatever is larger. But this does not fully define the term big data. Even a small amount of data can be called big data d

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Good question! Data Science and Big Data are probably the most popular terms used in the tech industry right now.

Let's first understand, what is Big Data?

Big Data is the collection of data that you cannot store or process using the traditional database system within the given time frame. There are many misconceptions when referring to the term big data, we use the term big data to refer to data that is in gigabytes or terabytes or petabytes or whatever is larger. But this does not fully define the term big data. Even a small amount of data can be called big data depending on the context in which it is being used.

If you don't like reading, here is an IntelliPaat video for you to watch. However, I would recommend that you read the answer for a detailed explanation.

For instance:

When you try to attach a file, which is 100MB in size via email, we will not be able to do so as the email system would not support an attachment of this size. Therefore, this 100 MB of data regarding the email system can be called Big Data.

If you want to understand what kind of data you can classify as Big Data, you have 5V for that. The 5 Vs of Big Data include:

  • Volume

Today, most of the data that is generated is of a very high volume. And over the last decade with the evolution of technology, that data is getting bigger and bigger.

  • Speed

Due to the popularity of many web-based applications, the data generated by them also arrives at a higher rate. For example, YouTube uploads about 300 hours of video every minute. Now imagine the amount of content that is loaded each day.

  • Variety

Different types of data are being generated from various sources. And we take care of these different types of files, all at once. This data includes a variety of files such as tabular columns, images, videos, audio, log tables, and more.

  • Veracity

Not all data is clean and accurate. Sometimes we all have data that is incomplete or not up to scratch.

  • Value

The data that is generated must have some value. It shouldn't be gibberish.

How do you extract meaningful data from previous data?

This is using Data Science. By using Data Science, you can analyze that data to obtain meaningful information from it.

Now, let's talk about data science. What is data science?

Data science is the study or processing of data, which leads to meaningful insights for an individual or a company. For example: based on historical sales data, the company wants to predict how many sales it can make in the next month.

So answering questions like these requires the use of Data Science.

Data science is the study of data, this data can also be in the form of Big Data. So, to sum it up, Big Data is the fuel data science needs to come up with meaningful insights.

Let's take an example where Big Data and Data Science work together:

Here, the role of data science enters the picture. Data science combines many skills such as statistics, mathematics, and business domain knowledge and helps the organization find ways to:

  • reduce costs,
  • enter a new market,
  • tapping into a different demographic,
  • measure the effectiveness of a marketing campaign,
  • launch a new product or service, etc.

Therefore, regardless of the industry vertical, data science is likely to play a key role in the future success of your organization.

Analysis of data

Data analysis refers to the quantitative and statistical methods that are used to derive meaningful information from the data. Data is extracted, stored and analyzed to study various patterns and trends of behavior.

Wondering how it differs from data science?

Data Science is a general term that is part of Data Analytics.

Hope this answers your question. Please comment if you have any questions.

Of course, data science is difficult (I'll tell you when it will be easy).

Data science is difficult because it is constantly changing, as time goes on more and more things are included in the gamut of data science and to a beginner it would seem very overwhelming.

Right now everything is affected by data science because we have data everywhere.

I've seen people trying to learn data science just to get a great job as this is what happens the most right now and I always suggest this to them:

Explore data science use cases and find at least one use case that you liked v

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Of course, data science is difficult (I'll tell you when it will be easy).

Data science is difficult because it is constantly changing, as time goes on more and more things are included in the gamut of data science and to a beginner it would seem very overwhelming.

Right now everything is affected by data science because we have data everywhere.

I've seen people trying to learn data science just to get a great job as this is what happens the most right now and I always suggest this to them:

Explore data science use cases and find at least one use case that you really liked. It can be anything like:

  1. Stock market prediction.
  2. Optimization of a plant's supply chain.
  3. Detect cancer through images.
  4. Building a chatbot.

and many more.

Once you have a few use cases that you are really connected to, start exploring what the skills are needed to learn the algorithms to solve those cases.

Once you do this, data science is easy because you are learning something that you are passionate about and every time a new technique is published to solve a problem instead of thinking, gosh! I have to learn this too to get a job, instead you'd be thinking: Wow! We have a new technique that will better solve my problem.

The beauty of data science is that once you are fairly familiar with the basics of math, statistics, and programming to solve a data science problem that you like, any other data science problem can also be solved with a little interest and knowledge of the domain.

One must make sure they learn the basics very hard first - learning in a structured way is important to really understand the nuances.

Let me take my case - I'm a trading graduate who loved the idea of ​​stock market prediction and supply chain optimization before I started learning data science and it wasn't that difficult because I learned it in a structured way and I was very passionate about it. them. Later, when I got to know other cool use cases like image analysis and text mining, I started exploring them too and realized that I already knew a lot about these things because the fundamentals are the same.

Soon,

Data science is easy if you learn it in a structured way, preferably keeping in mind some use cases that you are passionate about.

It depends. Sorry, but that's the truth.

But what does it depend on? Three things.

  1. You. Are you analytical? Do you enjoy solving complex problems with machine learning / statistical analytics methodologies? Do you enjoy programming? Are you curious and excited about a career of constant learning? Do you enjoy communicating complex information to people who don't get it?
  2. The job. Data science / analytics / analyst jobs vary substantially. Some jobs have more to do with data integration and data architecture. This would suit someone interested in the data side of IT. Some are more focused on reporting simple m
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It depends. Sorry, but that's the truth.

But what does it depend on? Three things.

  1. You. Are you analytical? Do you enjoy solving complex problems with machine learning / statistical analytics methodologies? Do you enjoy programming? Are you curious and excited about a career of constant learning? Do you enjoy communicating complex information to people who don't get it?
  2. The job. Data science / analytics / analyst jobs vary substantially. Some jobs have more to do with data integration and data architecture. This would suit someone interested in the data side of IT. Some are more focused on reporting simple metrics from a SQL server or a BI interface. These are suited to business-focused individuals with more limited skills (and interest) in data science. Others focus more on complex modeling. These jobs would have more statistics and machine learning skills. In all cases, the ability to communicate value and demonstrate leadership potential would be important for career progression. So which of these do you find interesting?
  3. The organization. Some organizations are well equipped for analytics. It requires a culture of data participation and adequate investment in IT infrastructure. The worst positions are those where you lack both and are expected to do like MacGyver and create algorithms from junk data and sell them to interested parties with no interest in ever applying them.

To put it in context, I work in a market research environment as an analyst. I work with survey data and do everything from weighting data sets to be representative to running statistical and machine learning models in SPSS and R. I would like to dive deeper into the data science side (more work in R and some exposure to Python as well as more experience with text modeling, etc.) but that doesn't mean I am lacking in challenges at work. The organization has a culture of commitment to data and I work with talented colleagues who push me to do my best. This suits me well, as I love the challenge of analytics, especially communicating complex modeling results to people who are struggling to understand them. So it certainly fits well.

Do you want to enter the data science / analytics space? Compare your interests. It is certainly NOT the ideal career path for everyone, but it is rewarding if it aligns with what interests you.

When choosing an online platform, it is important to understand the different styles of online learning that are offered today:

Learn at your own pace - Successively unlocked learning modules

Prerecorded Lectures: Watch an Instructor Videos with Materials to Work With

Live Online - Live classroom environment with instructors and other students delivered online

Each type of learning style has its pros and cons. While self-paced learning and prerecorded lectures are generally cheaper options, you need to be highly motivated to complete the course, and without instructors it is difficult

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When choosing an online platform, it is important to understand the different styles of online learning that are offered today:

Learn at your own pace - Successively unlocked learning modules

Prerecorded Lectures: Watch an Instructor Videos with Materials to Work With

Live Online - Live classroom environment with instructors and other students delivered online

Each type of learning style has its pros and cons. While self-paced learning and prerecorded lectures are generally cheaper options, you must be highly motivated to complete the course, and without instructors, it is difficult to get real-time feedback or answers to any questions you may have. . Many students in these types of programs end up not completing the course or do not fully understand the material.

Live online courses generally cost more than the other two; however, they provide students the opportunity to interact with their educators and classmates, just like in a classroom, but with the added flexibility of learning from anywhere in the world. This way, you can still learn online, but without compromising the engaging experience that comes with in-person learning. This method often leads to better results for students with higher completion rates and better knowledge retention.

Additionally, given the complex nature of data science, having the ability to ask questions, get feedback, and work on projects in real time increases your ability to understand the tools and concepts within data science.

I work for BrainStation, which specializes in providing a live online teaching experience. Through our data-driven learning portal, Synapse, we provide students with a seamless online learning experience. The portal allows students to attend live lectures, interact with peers and educators, and access resources such as class-specific learning outcomes, lecture slides, and additional training materials.

Our data science course is taught by industry professionals who constantly update course content with the latest concepts to ensure students have the highest skills, experience, and confidence to excel.

If you are interested in learning more or would like to access our free course package and preparation course to become familiar with the content, take a look at our data science course page. Good luck learning!

It's a well-known fact that data science is one of the hottest career options these days, thanks to the hype surrounding the data scientist job title. It has sparked increased interest in the field of data science from both working and college-bound professionals. To cope with the growing demand for data science professionals, many institutions have begun offering a master's degree in data science. If you plan on earning such a degree too, here are ten facts to consider before spending your time, effort, and money.

# 1. Before entering a data science

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It's a well-known fact that data science is one of the hottest career options these days, thanks to the hype surrounding the data scientist job title. It has sparked increased interest in the field of data science from both working and college-bound professionals. To cope with the growing demand for data science professionals, many institutions have begun offering a master's degree in data science. If you plan on earning such a degree too, here are ten facts to consider before spending your time, effort, and money.

# 1. Before entering a data science master's degree, you need to make sure you are genuinely interested in what the program would entail. For example, a professional might try to find an opportunity to gain some experience working with data to gain exposure, while a student might try to take a statistics class.

# 2. These days, many data science master's programs are taught online, which means it has become easier than ever to learn the skills necessary to become a data science professional. You will be able to enjoy a lot of flexibility in terms of studying when you want, working at your own pace, choosing a course schedule that suits you best, etc.

# 3. Having a master's degree in data science is certainly an effective way to build data science skills, but it is not a prerequisite for starting your data science career. It is possible to enter the field without having a master's degree in data science.

# 4. A master's degree in data science is very important when applying for a position, but not having a master's degree will prevent you from getting that job. For example, some tech giants may require applicants to have a master's degree in data science, while other companies may not have those stringent criteria.

# 5. While data science professionals are already in high demand, having a master's degree in data science could further enhance your chances. Other than that, you will be in a better position to negotiate your benefits.

# 6. If you really want to apply for a master's degree in data science, you must first decide which path you want to take. If you're willing to go back to school, earning such a degree can help you define the path in good measure.

# 7. Some of the data science master's programs are still in the process of developing the right curriculum that combines computer science, mathematics, and statistics, and there is a wide range in terms of breadth of knowledge offered, quality of the program, etc. In addition, in addition to requiring an investment of a minimum of one to two years, they can cost thousands of dollars.

# 8. Some data science master's programs, especially newer ones, may risk overpromising students and not meeting future employment requirements.

# 9. If you can complete a full data science bachelor's curriculum that involves statistical, computational, and professional practice aspects, it could be more comprehensive than a data science master's program.

# 10. If your goal is to earn a Ph.D., you should look no further than a data science master's program.

Final conclusion

You have probably already understood that it will be your decision if you opt for a master's program in data science. Please carefully consider the above information and make an informed decision based on your future goals.

Yes, absolutely, the 365 Data Science program is the best for beginners. I would recommend this program to anyone who wants to learn data science from scratch. This program is very useful for those looking to grow in the data science field and hone their skills. In addition, their teaching assistance is highly qualified and they are ready to support you 24/7. In addition, the course is divided into small modules and covers all topics from the basics such as Microsoft Excel and programming languages ​​such as R and Python and SQL and mathematics and statistics from the very same

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Yes, absolutely, the 365 Data Science program is the best for beginners. I would recommend this program to anyone who wants to learn data science from scratch. This program is very useful for those looking to grow in the data science field and hone their skills. In addition, their teaching assistance is highly qualified and they are ready to support you 24/7. Also, the course is divided into small modules and covers all topics from the basics like Microsoft Excel and programming languages ​​like R and Python and SQL and math and statistics from the basics that even a layman can understand very well.

This program benefits beginners:

  1. has no prior data science or machine learning experience
  2. You don't know of any analytical or programming tools like GitHub, R, Python, SQL, Power Bi, etc.
  3. No prior knowledge in subjects such as mathematics, statistics
  4. A person who does not have exposure in some of the sections like probability and algebra.

    I am really impressed with the structure of the course and the live classes helped me a lot in dealing with R-Language and Python. The sessions and the contents were very well instructed and delivered. There are study notes in downloadable formats that are very helpful. The course is well designed for beginners who are new to this field. The online learning platform is very easy to use. The content is really amazing. so, what are you waiting for ? Enroll in the course and happy learning.

Based on my experience working and leading data science projects, I would say that there are 3 criteria that define a good project. These are:

  1. Clear objectives. You can't solve a problem if you don't understand it. Since most of the time the projects we get are designed by ordinary people, the requirements are often vague and sometimes based on fantasy. This is due to a lack of experience with machine learning in general. Most normal people don't understand data science or machine learning and have a hard time finding clear specifications for what they want the final model to do. This can cause a lot of fruit.
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Based on my experience working and leading data science projects, I would say that there are 3 criteria that define a good project. These are:

  1. Clear objectives. You can't solve a problem if you don't understand it. Since most of the time the projects we get are designed by ordinary people, the requirements are often vague and sometimes based on fantasy. This is due to a lack of experience with machine learning in general. Most normal people don't understand data science or machine learning and have a hard time coming up with clear specifications for what they want the final model to do. This can cause a lot of frustration and delays. Hence why this is my number one criterion. The first thing to do before you start coding or searching for data is to define the goals of your project and what you need to achieve to avoid all the headaches related to miscommunication.
  2. Well defined success metrics. If you are a data scientist, you can view this as your loss function. This is basically how accurate the model should be. Since things like precision and mean square error are super abstract project managers, clients often have a hard time deciding how to measure the success of a ML / data science project. Often you will hear non-MLs ask for high precision when in reality other metrics, such as recovery, might be more important. These also need to be very well defined as you could end up optimizing and building a model for the wrong purpose.
  3. Enough high-quality data. I don't remember how many times a client approached me and asked me to build a model with no data or with a few hundred samples. In most cases, this is not the customer's fault if you have never worked on a data science project before it is really difficult to decide how much data you will need. It is even difficult for experts at times, much less for the non-technical manager of your company. The importance of data should never be underestimated, as that is the secret sauce that makes your model good. Having too little will make your model tasteless, having too much low-quality material will make everyone want to leave.

Today there are many certification courses available for both students and professionals. Finding the best institute not only in Delhi but anywhere in India is difficult, but then there are these online courses that you can sign up for and be at your location but still study. I thought of it as an opportunity because I don't have to move to a different location for additional studies.

Since there are many institutes online these days, it is difficult to choose the best one, several parameters must be considered before thinking about enrolling in any course.
However, I will share my parameters with you, c

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Today there are many certification courses available for both students and professionals. Finding the best institute not only in Delhi but anywhere in India is difficult, but then there are these online courses that you can sign up for and be at your location but still study. I thought of it as an opportunity because I don't have to move to a different location for additional studies.

Since there are many institutes online these days, it is difficult to choose the best one, several parameters must be considered before thinking about enrolling in any course.
I will share my parameters with you, however you can still add more if needed.
• Practice-based education: students must work on real tools and real-world problems for the duration of the program. The institute should focus on data scientist delivery and not just certification of being a data scientist.
• Industry Preparation: The main focus of the institute should be to prepare students for industry, giving them exposure to industry projects and relevant tools used in industry.
• Internship: If the institute does not offer an internship, it is useless because the internship gives students an idea about what the role and responsibility of being a data scientist in industry is.
• Quality ecosystem: The program's curriculum must be well developed. It should be more practical than theory. Faculties must have a minimum experience of 8 years. A friendly bond between the institute and the student.
• Placement: The institute that provides placement assistance after completion of the program is a scam. You should look for an ideal institute that offers placements as soon as the program is completed.
More parameters can be added to those mentioned above.
I was in the same situation 5 months ago. I finished researching when each of my parameters was met. Data Trained Education is the institute that caught my attention.
They offered practical exposure rather than theory, but still covered all modules of the syllabus. They are offering a 6-month unpaid internship to each student to gain hands-on experience and industry exposure. They have collaborated with IBM and this provides all students access to IBM Watson Cloud Lab with cloud credits worth USD 1200. They also provide placement guarantee over money back guarantee.
These offerings define the trust they have in your content and in the program they have created.

Long and short answer, the following are the best data science courses:

  1. Machine Learning from Andrew Ng, Coursera,
  2. Deep Learning Course by Andrew Ng, Coursera,
  3. Neural Network by Geoffrey Hinton, Coursera

To begin with, one should try participating in the hackathons at kaggle, Analyticsvidhya. This will help one to learn and grow very fast.

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