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I chose the wrong data science course and how you can learn from my mistake

Posted on by Imarticus Learning

I chose the wrong data science course and how you can learn from my mistake

The field of Data Science is ever-growing. People from all walks of the IT sector are getting trained in data science as it seems to be one of the most promising career paths of the future, If you know your way around the field, then you know that data science is exactly that, a science. Not just a part of IT and business. A lot of people are opting for data science online training to build a solid career in data science. But what if I told you, that one of the biggest mistakes you can make is choosing the wrong data science online training course and wasting your precious time? Not only that but there are a lot of mistakes you can make while self-studying for data science. This article can help you refrain from making these mistakes and tell you which path will take you to the top fast. 

Some Common Mistakes People Make while Learning Data Science 

Now that we have delved into the topic, let us talk about some of the most common mistakes people make out of misconception while learning data science. 

Choosing the Wrong Course

online learning

You will have a lot of choices to choose from if you want to learn data science. There are many courses, books, videos and blogs at your fingertips. But overwhelming yourself with options is not the right way to go. Instead, focus on the courses that match your learning style and choose books and blogs that seem easy to you.

You have to slowly level up in this field and it can take a long time. One of the courses that can help you in this field is the IHUB Data Science and Machine Learning certification course. Which is an IIT Data Science Certification Course to help people get started on their career path as data scientists. It is a comprehensive course that teaches you the basics of data science and machine learning. 

Lack of Projects 

Learning data science includes a lot of studying, but it also involves a lot of hands-on projects. A lot of the time, people get so immersed in studying that they forget that in order to have a good career in data science, they will also need practical experience in the field. The most important thing to do alongside studying is making sure you work on a project for each thing you learn. That way, you will have real-life experience of what you are studying. 

Lack of Time Management and Planning 

One of the mistakes aspiring data scientists make while learning the subject is underestimating how much time and commitment it takes to properly learn data science. Another one is lacking a solid plan. Depending on your study methods and what you want to learn, you have to have a future plan for your study course. It should include all the courses, books, topics and projects you want to take on. This way, you will understand the subject better and also manage your time well. 

Too Many Research Papers 

Last but not the least, make sure you do not get too lost in research papers associated with your topics. It is normal to want to expand your horizons and learn about what the experts have to say about the topic you are pursuing. But often these high-end research papers can be too hard to comprehend for new learners. Better keep these for when you have a better understanding and grasp of the subject. 

Conclusion 

Now that you have learned which mistakes to avoid while learning data science, you can go about your way to pursue the subject. Remember that you can always learn from your mistakes down the line and make yourself better in the future. Data Science is a very specialised domain and you must be extra careful to avoid the mistakes that have been discussed. Making these mistakes can lead to you wasting your precious time, money and other resources. 

For instance, if you do not take up real-world projects and simply keep learning from study material or lessons, you will lack the experience to be job-ready. Companies are also more likely to hire you if you have deployed projects rather than how many courses you have completed. However, a well-rounded certification such as the one offered by Imarticus can help out a lot. 

I compared top data science and machine learning courses and here’s what I think

Posted on by Imarticus Learning

I compared top data science and machine learning courses and here’s what I think

We’ve all seen the data science and machine learning courses advertised on the internet. But what is a data science course, exactly? And should you be doing a machine learning course instead? I compare two popular data science and machine learning courses to help you decide if one is right for you.

Data Science

It is the in-depth examination of large amounts of data in a company’s or organization’s repository. This research includes determining where the data came from, analyzing its content, and determining how this data can get used to help the company grow in the future. 

An organization’s data is always in one of two forms: structured or unstructured. When we analyze this data, we gain valuable information about business or market patterns, which gives the company a competitive advantage because they have increased their effectiveness by recognizing patterns in the data set.

Machine Learning

It is a subset of data science and is one of the most powerful tools in your arsenal. It’s also sometimes known as artificial intelligence or machine learning, but don’t let that confuse you. They all mean the same thing: techniques for making predictions about something based on past data.

If you’re new to this topic, consider what machine learning can do:

  • Predict future customer behavior by looking at past purchases (like online shopping).
  • Make recommendations based on users’ purchase history and preferences (like Netflix recommending movies based on previous viewing patterns).

What Makes These Two Techniques Different?

Data science includes machine learning. It’s more about the process of data analysis, while machine learning is more about the process of data prediction.

Data scientists rely on statistics and mathematics to analyze large amounts of information from diverse sources, but machine learning only uses math. This means that when you need to predict something based on past events or trends, you can use either technique—you just have different tools for each task!

How to choose between Data Science and Machine learning?

First, you need to know that there are two main types of data science: descriptive and predictive.

Descriptive data scientists analyze large datasets to find patterns, explain trends and predict outcomes. You can use them for marketing research, fraud detection, or predicting the weather.

Predictive models use machine learning algorithms like neural networks (a type of artificial intelligence) or decision trees (a tree-based model) that make predictions based on collected training sets—the same results over time. 

Machine learning seems more applicable in some cases (like spam filtering), while data science might have better predictive power. In any case, both of these fields are extremely exciting and evolving rapidly!

Discover Data Science and Machine Learning Career with Imarticus Learning.

Students can start their careers in data science with this certificate program in data science and machine learning. Through this curriculum, students will learn the fundamentals of data science and machine learning and the knowledge and skills required to apply these concepts in the real world.

Course Benefits For Learners:

  • This five-month program, designed by IIT faculty members, will teach students how to use Python to understand data mining and machine learning methodologies.
  • India’s top educators will deliver this data science certification course live online.
  • Students will develop a solid foundation in data science with the assistance of our online data science program.

Contact us through chat support, or drive to our training centers in Mumbai, Thane, Pune, Chennai, Bengaluru, Delhi, Gurgaon, or Ahmedabad.

A step-by-step guide to building a data science portfolio

Posted on by Imarticus Learning

In the field of data science, it is integral that you have an impressive portfolio if you want to get a good job according to your skill level. Employers are constantly sceptical about people lying on their resumes and that is why they require the candidates to have proof of the work they have done previously before they hire them. You might have a lot of experience in the field as an intern or as an employee. But to have a good career in data science, you will have to convey that well to your future employers, and that is where a good portfolio comes in. 

Why is a Good Portfolio Important for a Data Science Professional? 

You might be wondering why is a good portfolio so important in the field of data science. The answer is pretty easy: because that is what employers are looking for. A portfolio is used to demonstrate your skills and all the certification course in data science, or work you have done previously. It impresses employers when they see that you not only have the knowledge, skills and certifications but also hands-on experience in the field. This boosts their confidence in your capabilities and also makes the hiring process much easier. They will interview you, but having a good portfolio will impress them and also make it clear to them that you take this career path seriously and that you have the necessary skills to back you up. That is why, if you are looking to get a job in the data science industry, you should invest in a good portfolio that will highlight your skills and experiences. 

Some Tips and Tricks to Make Your Data Science Portfolio Outstanding

Now that we have explained why you need to have a stellar portfolio to build a great career in data science, let us talk about how you should go about doing that. In simpler words, what are some of the ways to make your portfolio stand out from the rest and make sure you get the job you are intending to.

Exhibit Your Technical Skills 

As you can understand, this is probably one of the most important parts of building a great portfolio. You have to include the important technical skills that you have in your portfolio, but that does not mean you have to add all of them. You can add the more important ones and the ones that you feel are more important for the job roles while leaving the simpler, normal and cookie-cutter skills out of it. This shows off your better skills and makes your portfolio more attractive. 

Be Passionate 

Being passionate is important as it can persuade your employers. In the data science sector, it is important for employers to see that you are passionate about what you do. It tells them that you will take the job more seriously. 

Personal Brand 

Your portfolio is only a part of your identity. Your employers can find out a lot about you through your social media, or LinkedIn and GitHub profiles. Make sure that you link all of them in your Resume and portfolio to make your personal brand appealing to them as well. Your profiles should also convey your professionalism and passion for your chosen career path. 

Conclusion 

Now that we have covered all the bases about how to build a good portfolio, let us talk about how you can improve your skills and learn data science. You can add some amazing data science and machine learning certifications to your portfolio to make it more attractive to your employers. The IHUB IIT Data Science certification course which also teaches machine learning, will help you improve your portfolio for your next job. 

What Is A Cluster Analysis With R? How Can You Learn It From A Scratch?

Posted on by Imarticus Learning

What is Cluster analysis?

Cluster means a group, and a cluster of data means a group of data that are similar in type. This type of analysis is described more like discovery than a prediction, in which the machine searches for similarities within the data.

Cluster analysis in the data science career can be used in customer segmentation, stock market clustering, and to reduce dimensionality. It is done by grouping data with similar values. This analysis is good for business.

Supervised and Unsupervised Learning-

The simple difference between both types of learning is that the supervised method predicts the outcome, while the unsupervised method produces a new variable.

Here is an example. A dataset of the total expenditure of the customers and their age is provided. Now the company wants to send more ad emails to its customers.

library(ggplot2)

df <- data.frame(age = c(18, 21, 22, 24, 26, 26, 27, 30, 31, 35, 39, 40, 41, 42, 44, 46, 47, 48, 49, 54),

spend = c(10, 11, 22, 15, 12, 13, 14, 33, 39, 37, 44, 27, 29, 20, 28, 21, 30, 31, 23, 24)

)

ggplot(df, aes(x = age, y = spend)) +

geom_point()

In the graph, there will be certain groups of points. In the bottom, the group of dots represents the group of young people with less money.

The topmost group represents the middle age people with higher budgets, and the rightmost group represents the old people with a lower budget.

This is one of the straightforward examples of cluster analysis. 

K-means algorithm

It is a common clustering method. This algorithm reduces the distance between the observations to easily find the cluster of data. This is also known as a local optimal solutions algorithm. The distances of the observations can be measured through their coordinates.

How does the algorithm work?

  1. Chooses groups randomly
  2. The distance between the cluster center (centroid) and other observations are calculated.
  3. This results in a group of observations. K new clusters are formed and the observations are clustered with the closest centroid.
  4. The centroid is shifted to the mean coordinates of the group.
  5. Distances according to the new centroids are calculated. New boundaries are created, and the observations move from one group to another as they are clustered with the nearest new centroid.
  6. Repeat the process until no observations change their group.

The distance along x and y-axis is defined as-

D(x,y)= √ Summation of (Σ) square of (Xi-Yi). This is known as the Euclidean distance and is commonly used in the k-means algorithm. Other methods that can be used to find the distance between observations are Manhattan and Minkowski.

Select the number of clusters

The difficulty of K-means is choosing the number of clusters (k). A high k-value selected will have a large number of groups and can increase stability, but can overfit data. Overfitting is the process in which the performance of the model decreases for new data because the model has learned just the training data and this learning cannot be generalized.

The formula for choosing the number of clusters-

Cluster= √ (2/n)

Import data

K means is not suitable for factor variables. It is because the discrete values do not produce accurate predictions and it is based on the distance.

library(dplyr)

PATH <-“https://raw.githubusercontent.com/guru99-edu/R-Programming/master/computers.csv”

df <- read.csv(PATH) %>%

select(-c(X, cd, multi, premium))

glimpse(df)

Output:

Observations: 6,259

Variables: 7

$ price  <int> 1499, 1795, 1595, 1849, 3295, 3695, 1720, 1995, 2225, 2575, 2195, 2605, 2045, 2295, 2699…

$ speed  <int> 25, 33, 25, 25, 33, 66, 25, 50, 50, 50, 33, 66, 50, 25, 50, 50, 33, 33, 33, 66, 33, 66, …

$ hd     <int> 80, 85, 170, 170, 340, 340, 170, 85, 210, 210, 170, 210, 130, 245, 212, 130, 85, 210, 25…

$ ram    <int> 4, 2, 4, 8, 16, 16, 4, 2, 8, 4, 8, 8, 4, 8, 8, 4, 2, 4, 4, 8, 4, 4, 16, 4, 8, 2, 4, 8, 1…

$ screen <int> 14, 14, 15, 14, 14, 14, 14, 14, 14, 15, 15, 14, 14, 14, 14, 14, 14, 15, 15, 14, 14, 14, …

$ ads    <int> 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, …

$ trend  <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…

Optimal k

Elbow method is one of the methods to choose the best k value (the number of clusters). It uses in-group similarity or dissimilarity to determine the variability. Elbow graph can be constructed in the following way-

1. Create a function that computes the sum of squares of the cluster. 

kmean_withinss <- function(k) {

cluster <- kmeans(rescale_df, k)

return (cluster$tot.withinss)

}

2. Run it n times

# Set maximum cluster

max_k <-20

# Run algorithm over a range of k

wss <- sapply(2:max_k, kmean_withinss)

3. Use the results to create a data frame

# Create a data frame to plot the graph

elbow <-data.frame(2:max_k, wss)

4. Plot the results

# Plot the graph with gglop

ggplot(elbow, aes(x = X2.max_k, y = wss)) +

geom_point() +

geom_line() +

scale_x_continuous(breaks = seq(1, 20, by = 1))

What Is a Real-time Processing in a Big Data Use Case?

Posted on by Imarticus Learning

Breaking down Real-time Processing

In today’s digital era, people are accustomed to real-time information that helps them make more informed decisions by factoring in the latest updates. Businesses in contemporary need real-time information to remain competitive in the market and improve their offerings.

best Data Analytics courses in IndiaLeveraging big data has become indispensable for businesses as it helps them provide valuable insights into the market. A big data career is also considered one of the most sought-after career choices given the demand for the same.

Real-time data processing systems help to factor in rapidly changing variables and relevant data to provide instant output so that any unnecessary delays can be avoided.

Some of the most prominent examples of real-time processing include order management for e-commerce websites, online ticket booking, financial fraud detection, radar range system, etc. A continuous stream of data is required to produce instant output in real-time. The most prominent benefit of real-time processing is that it helps to provide instant results and ensures that everything is up to date.

Real-time Big Data Processing Applications

Some of the most prominent applications of real-time big data processing in the contemporary include the following.

Online Commerce

Online commerce or e-commerce platforms heavily rely on real-time big data processing given the nature of business. It helps to solve issues related to customer service, inventory management, purchase pattern detection, personalized offerings, reduction of churn rate, etc. E-commerce companies also use real-time data processing to improve their logistics; they identify the nearest warehouse to effectively reduce delivery rates. It also helps to optimise the price and increase the sales volume by analyzing tons of data in real-time.

Social Media Networks

Gone are the days where people used to receive news the next day after the occurrence of a particular event. Social media networks have improved the flow of information and one can instantly receive any new update in real-time avoiding any delay in communication. Given the flow of information on social media platforms, real-time processing of data is almost a necessity. Some of the important functions performed by real-time data processing in the case of social media networks include classification of user-generated data or content, speeds up real-time trending, server functionality analysis, etc.

Healthcare

The healthcare industry also heavily relies on the use of real-time data processing systems. Some of the most common applications in this sector include real-time patient monitoring to assess crucial changes, wearable sensors and devices help to make life-saving decisions using real-time data. A majority of the data in the healthcare domain is usually unstructured and real-time big data processing helps to provide a detailed overview. It also assists in prescribing relevant medicines and avoids any unnecessary expenses.

Conclusion

Real-time big data processing has become popular in the last decade and is almost a necessity for a business that wants to create a sustainable brand in the long term.

best data analytics certification courses in IndiaThe big data career is also gaining traction with the growing demand for data analysts who can assist in the real-time processing of big data.

One can opt for data science courses from reputed institutions like Imarticus Learning to boost their job prospects and build a career in this field.

How an Artificial Intelligence and Data Science Work For an Online Conference?

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Artificial Intelligence and data science are the driving force behind most of the online conference tools available now. The more advanced the AI used, the better its experience is for the users. The advancement enables the user to have a more personalized and better quality video feed and experience.

Artificial intelligence and video conferencing

Already there is the option to replace and blur the background as per convenience. The use of Artificial Intelligence and data science in this arena is much more than that. Background manipulation is only the tip of the huge iceberg.

There are other features such as translation for those who are not familiar with the language spoken and it works either way when implemented properly. There is another possibility of transcribing the same to make it easier to understand.

The noise reduction in the background comes as another advantage where there could be distractions, especially when it is working from home scenarios.

All of these features make online conferencing more convenient and comfortable which is crucial at the moment. As work from home is becoming more normal than ever and online conferencing is the big ‘thing’ now, it needs all inputs from the Artificial Intelligence industry to make it simpler.

How data science comes into the picture?

Although Artificial Intelligence and data science sound similar, they are two sides of the same coin. While AI is the major force behind online conferencing, data science is no less important.

Data science comes in handy when the employee’s data needs to be analyzed. For eg, in order to find the ideal time for a conference, the log in log out time of the employees would be useful. By analyzing this data, a more appropriate time could be deduced where there is a possibility of maximum participation.

The chatbots powered by Artificial Intelligence could also need the backup of data science to implement it more effectively. It helps to communicate with multiple users at the same time and reduces the call time for both sides.

Amazon has implemented these chatbots and has found success with the same. The users have also expressed satisfaction when it comes to minor issues which do not usually need waiting in line to talk to customer care. This is a perfect example of AI and data science in use.

Learn Artificial Intelligence and data science

AI and data science are raising the bar with their advancements and specializations. Its popularity and demand are at an all-time high with numerous job opportunities in both fields.

People are queuing up to enroll in an Artificial Intelligence course at any cost. Specializing in this area is fruitful for the professionals and newcomers equally.

Artificial Intelligence and Data ScienceStarting a data science career may have been difficult in the past but it is a golden opportunity for all right now. Unlike Artificial Intelligence, data science is more beneficial for working professionals to maintain and advance in their careers. It helps them climb the corporate ladder a lot easier than ever. Moreover, there are multiple branches in data science that one can easily choose the most appropriate for their career.

Wrapping up

Though data science and Artificial Intelligence were present in various aspects of life, the pandemic has made it more familiar for the common man. Online conferences are only one such aspect but the one that has made the maximum impact on everyone. There was a time when such concepts were frowned upon but the increasing use of smartphones and the user-friendly approach of such technology has made it possible to make online conferencing a normal and ordinary household term.

Which Career Is More Promising: Data Scientist or Software Developer?

Posted on by Imarticus Learning

As per a study, about 2.5 quintillion bytes of data are produced every day. The number is about to increase by many folds in the future. The pace at which data is being generated demands more data science professionals. That’s the reason data science jobs are now regarded as one of the highest-paid jobs across the globe. And, people are joining data science training to explore a career in data.

As, firms are hiring employees who can build technical applications, and collect, analyse and predict data. That’s the reason students are enroling in data science training courses that are best and give job assurance.

Companies are keen to employ data scientists, data analysts, web developers, full-stack developers, and much more technology professionals. According to Imarticus Learning and Analytics India Magazine’s research, there will be a 30.1% increase in data science and analytics jobs in 2022 compared to 2021.

However, more competition in Information Technology requires software career enthusiasts to learn the latest technology for survival. The primary responsibility of data scientists is business analytics. They also work on constructing data and software products using algorithms. The demand for data scientists is high due to the shortage of skilled people for start-ups and corporates. Global companies are searching for professional data scientists to up their game in this competitive world.

Who is a data scientist?

Data scientists collect and interpret large amounts of structured and unstructured information. They analyse, process, and model data before interpreting the findings to develop actionable plans for businesses. The data scientist uses computer science, statistics, and mathematics for data interpretation.

Who is a software developer?

Software developers are the thinkers and doers behind computer programs of all kinds. While some software developers specialise in a single program or application development, others build massive networks or underlying systems that trigger and power other programs. As a result, there are two types of developers: applications software developers and systems software developers.

The approach of data scientists and software developers

To use and organise data in a structured or unstructured form, a data scientist becomes essential and can master the art with the right Data Science Training. Hence, there is a need for a data scientist, with sound knowledge of handling data that demands the best data science training and experience.

Software developers collaborate with computer programmers to test the convenience of using a software program. The component that powers digital systems is software which is the core element of computers. To be precise, software engineering can be termed as the application of engineering principles for designing, structuring, developing, and implementing software. An efficient software developer understands the client’s needs and then develops the software as a solution to the client’s needs.

Important tools used in software development and data science

To be productive, data science career aspirants should be knowledgeable about statistics, software programming, and analytical thinking. In addition, a data scientist should have the urge to learn and be updated about new programming languages.  Python, Swift, and Ruby, apart from mastering SQL, Spark, machine learning, and Hadoop are the most important to learn.

A software developer should know the latest programming languages along with a bachelor’s degree in computer science. A good understanding of computer programming by understanding the functionality of new tools is a plus. Along with various skills, a software developer should possess to flourish in the field of analytical skills. Followed by attention to detail, creativity, problem-solving techniques, and interpersonal skills are vital.

The pay scale for data scientists versus software developers

A report from Payscale.com reveals that the median salary for a data scientist is around 6,00,000. It is not much different in the US. According to a survey by Glassdoor, the median salary for a data scientist is $91470, which is great. So, it is evident that data science can offer ample money and growth. But, the salary also depends on the qualifications, skills, experience, and location. Firms also play a vital role in deciding the pay. 

Currently, the median salary for software developers is $1,00,80 and the growth in employing software developers is set to increase by 24% in a decade. In addition, an estimate states that there are 3.7 billion mobile users worldwide. It demands newer and better applications for smartphones and tablets. It increases the demand for software developers globally. Factors that affect the salary of a data scientist are similar to those of software developers. 

Roles and job titles for a career in data science versus software development

A few important job titles that a data scientist can do are data architect, data scientist, and business analyst. In fact, the roles of data administrator, and business intelligence manager are also in demand. Global companies in the digital world, like Facebook, Twitter, IBM, and Apple, are racing to hire the best data scientists. So, the demand for data scientists is growing like never before.

Some of the interesting career options that connect to software developers are Computer programming. Computer system analysts, web developers, computer network architects, and computer hardware engineers are also on the list. Though data science is a relatively newer concept than software development, it is increasing rapidly. 

Conclusion

This article has showcased the digitisation, technology, data rise, and demand of technology professionals. The high demand for data scientists, and software developers have given rise to the skills gaps. If you are eager to learn new tools for efficient data handling, you can grab a chance to flourish as a data scientist in the long run. Enrol for the best data science training course and enter the world of data.
But, selecting the best institute for data science training is not easy.

You should enquire about the faculty, course curriculum, and the job assistance that they provide. It is always good to get interview assistance, career monitoring, and resume-building tips from the experts. If the institute provides all this then it is the best for the learners.

It should be your priority to learn coding and programming if you want to become a software developer. Furthermore, the amount of data being produced every day is high, which signifies long-term growth and attractive salary await a talented data scientist. So, in the end, we can say that both careers in data science and software development are promising. It depends on individual preferences and interests. Both, career options are good and offer career growth. 

For more details, you can also search for – Imarticus Learning.  You can drop your query by filling up a form from the site. You can also contact us through the Live Chat Support system. To explore more you can visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi, and Gurgaon.

3 Tips on Building a Successful Online Course in Data Science!

Posted on by Imarticus Learning

3 Tips on Building a Successful Online Course in Data Science!

The coronavirus pandemic is undoubtedly one of the biggest disruptors of lives and livelihoods this year. Thousands of businesses, shops and universities have been forced to shut down to curb the spread of the virus; as a result, massive numbers have turned to their home desks to work from and to tide over the crisis.

The pandemic has also influenced the surge of a new wave of interest in online courses. Over the past few months, many small and large-scale ed-tech companies have sprouted up, bombarding the masses with a wider range of choices than ever before. Many institutions have chosen to give out their courses at a minimal price and yet others for free. The format of these classes is different– hands-on, theoretical, philosophical, or interactive– but the ultimate goal is to take learning online and democratize it.

Naturally, it’s an opportune time to explore the idea of creating an online course– a data science online course, in particular, seeing as futuristic technologies will see a profound surge in attention come the next few years.

Here are a few tips to get the ball rolling on your first-ever online course in data science:

  • Create a Curriculum

Data science is a nuanced and complex field, so it won’t do to use the term in its entirety. It is important to think up what the scope of your course will be. You will need to identify what topics you will cover, what industry you want to target (if any), what tools you might need to talk about, and how best to deliver your course content to engage students.

education

General courses are ideal for beginners who don’t know the first thing about data science. This type, of course, could cover the scope of the term, the industries it’s used in as well as job opportunities and must-have skills for aspirants.

Technical courses can take one software and break it down– this is also a great space to encourage experiments and hands-on projects. Niche courses can deal with the use and advantage of data science within a particular industry, such as finance or healthcare.

  • Choose a Delivery Method

There are a plethora of ed-tech platforms to choose from, so make a list of what is most important to you, so you don’t get overwhelmed. Consider how interactive you can make it, through the use of:

  1. Live videos
  2. Video-on-demand
  3. Webinars
  4. Panels
  5. Expert speakers
  6. Flipped classroom
  7. Peer reviews
  8. Private mentorship
  9. Assessments
  10. Hackathons
    Education

The primary draw of online classrooms is also how flexible they are. Consider opting for a course style that allows students to learn at their own pace and time. Simultaneously, make use of the course styles listed above to foster a healthily competitive learning environment.

  • Seek Industry Partnerships

An excellent way to up the ante on your course and set it apart from regular platforms is to partner with an industry leader in your selected niche. This has many advantages– it lends credibility to your course, brings in a much-needed insider perspective and allows students to interact outside of strict course setups. Additionally, the branding of an industry leader on your certification is a testament to the value of your course; students are more likely to choose a course like yours if this certification is pivotal in their career.

EducationOther ways by which you can introduce an industry partnership include inviting company speakers, organising crash courses on industry software and even setting up placement interviews at these companies. The more you can help a student get their foot in the door, the higher the chances of them enrolling and recommending.

Conclusion
Building an online course in data science is no mean feat. However, it’s a great time to jump into the ed-tech and online learning industry, so get ready to impart your knowledge!

How Imarticus Helps For A Data Science Career in Pandemic Times?

Posted on by Imarticus Learning

Data-driven strategies have shot up in popularity after the coronavirus pandemic wreaked havoc on business plans. Data science is a key player in sustaining businesses not just now, but in the future when similar turbulent circumstances threaten to bring down shutters on previously stead organisations.

As a result, hundreds of companies across India and overseas are looking to add more data scientists to their repertoire. This comes from a need to drive more data-driven decisions and make businesses more resilient to change.

Here are some specific reasons that make a case for how choosing a data science career can be beneficial in times like these:

  • A need for general expertise

While previously companies favored data science specialists, today they prefer generalists and Jacks of all trades. While specialists come with in-depth knowledge and specific skill sets, they often cannot think beyond their domain. Companies today need someone who has skills to use across the board so that they can both learn on the job and be useful where they’re needed.

  • A need for understanding project flows

Many companies who are delving into data science now are probably unsure of their footing and their way forward.

Data Science CourseA data scientist is critical in companies like these, as they bring expertise to the table and understand the flow of projects much better than anyone else. With a data scientist at the helm, all other players in the process can fall into place. This reduces the pressure on upper management to figure out project flows; they can now leave it to the experts.

  • Higher chances for growth

Data scientist generalists are more likely to grow with the company– something many organisations prefer. Unlike a specialist, who already has a defined skill set, rookie data scientists can be shaped and molded into an ideal employee for the company. In the process, the data scientist becomes an intrinsic part of the organisation, learns business tactics and applications and develops skills through experience rather than through specializations. As a result, they become both experts and creative problem-solvers.

  • Immediate requirements

Businesses are struggling to stay afloat during the aftermath of the pandemic and are realizing their urgent need for data-driven business plans. As a result, many of them have put out feelers and immediate job offers for data scientists. This is in complete contrast to other fields that are seeing scores of job cuts, furloughs and pink slips, and goes to show that data science is only increasing in popularity.

It is worth keeping in mind that, despite recruitment into data science roles, many companies have slashed budgets and can’t afford to pay more experienced scientists at this stage. Rookie data scientists form the perfect compromise–  they’re eager to learn, have the necessary skills and can be accommodated within tighter budgets without reduced salaries.

  • Opportunities for upskilling

As rookie data scientists settle into their roles, many companies consider upskilling them for higher positions or specific technical projects.

Data Science CareerThis is an invaluable opportunity for fresh data scientists as the company takes care of all the costs and only asks for your attention and application in exchange. Adding a data science course to your CV will also help you get a leg up on the competition when you’re ready to switch roles or companies.

The final word

The data science landscape has shifted significantly in response to the coronavirus pandemic. As a result, rookie data scientists who are only just entering the field have a once-in-a-lifetime chance to make their mark and cement their place for when things stabilize.

15 Reliable Sources to Master Data Science

Posted on by Imarticus Learning

15 Reliable Sources to Master Data Science

Data Science is growing at a rapid pace and businesses have been dynamically benefitting from this. A lot of Data Science Courses are available at the Imarticus Learning Data Science Training Center. No doubt, the insights and knowledge of data science have helped business emerge a winner with better knowledge and insights available at their fingertips. Have a look at these 15 important blog resources with the highest number of followers if you are willing to understand and learn data science. These blogs have rich data science resources and won’t let you miss you anything in the world of data science.

  1. Reddit – It’s an American social news aggregation, web content rating and discussion website for everyone who loves to share content and satisfy their curiosity. The registered members at Reddit can submit content such as text posts or direct links and get opinions on the same. It’s a hugely popular website where everyone can participate because it’s simple and easy.

Frequency — About 84 posts per week

Facebook Fans: 1,108,745

Twitter Followers: 511K

2. Google News – Comprehensive and most dynamic up-to-date news coverage, aggregated from all over the world by google news. It’s a popular medium throughout the world since Google has become a most reliable name everywhere. It’s a reliable source of Data Science information where everything related to it will be at your fingertips.

Frequency — About 21 posts per week

Facebook Fans: n/a

Twitter Followers: 214K

3. Data Science Central – Now this is a platform where every kind of information is available in one place. It wouldn’t be wrong if we say that it’s the industry’s online resource for big data practitioners. And it’s damn popular among the practitioners. From analytics to data integration to visualisation, data science centre provides a community experience.

Frequency — About 24 posts per week

Facebook Fans: 1,013

Twitter Followers: 100K

4. KDnuggets I Data Science, Business Analytics, Big Data and Data Mining – Now, if you are looking for the most interesting and updated blogs on day to day evolution of the Big Data, then this is the place to be. Here, one can find the most interesting stuff on analytics, big data, data science, data mining and machine learning, not necessarily in that order.

Frequency — About 34 posts per week

Facebook Fans: 21,860

Twitter Followers: 96K

  • Kaggle I Data Science News – No Free Hunch – A competitive platform where companies and researchers post data while statisticians and data miners compete with each other to produce the best models for predicting and describing the data. It’s a popular platform where professionals compete with each other to come up with the best ideas that they have.

Frequency — About one post per month

Facebook Fans: 35,137

Twitter Followers: 89.1K

    • Revolution Analytics – An exclusive blog dedicated to the news and information of interest to the members of the community, who are deeply interested in analytics and relation disciplines. The blog is updated every US workday, with contributions from various authors.

Frequency — About six posts per week

Facebook Fans: n/a

Twitter Followers: 25.9K

  •  Data Science for Social Good – This social good data science does the work of training data scientists to handle the problems that matter. It effectively trains the data scientists to work on data mining, machine learning and big data.

Frequency — About one post per month

Facebook Fans: n/a

Twitter Followers: 20.5K

  • Data Camp – You can learn to be a data scientist from the comfort of your home through your browser with Data Camp’s data science blog. It’s a comfortable way where total information is available in one place, and you can pick up the topics that you want to master.

Frequency — About seven post per month

Facebook Fans: 340,109

Twitter Followers: 16.2K

9. Codementor – This blog tells you about the latest trends in data science. Here you can read tutorials, posts and insights from top data science experts and developers. This will eventually help you gain knowledge from experienced experts.

Frequency -About one post per month

Facebook Fans: 12,587

Twitter Followers: 22,1K

10. Dataversity – Data Science News, Articles & Education – Here, learn about the latest business intelligence news and get a thorough business intelligence education. This blog is focused more on the business side and understanding it is necessary from the business point of view.

Frequency – About one post per week

Facebook Fans: 6,312

Twitter Followers: 17.4K

11. Data science @ Berkeley I Online Learning Blog – If you are interested in an online course called professional Master of Information and Data Science (MIDS) from UC Berkeley School of Information.

Frequency — About one post per month

Facebook Fans: 14,804

Twitter Followers: 10.2K

12. Data Plus Science – This blog helps people find real answers in data science, quickly and effectively. So it’s a swift means of knowledge generation.

Frequency — About two posts per month

Facebook Fans: 2,932

Twitter Followers: 25.1K

13. NYC Data Science Academy Blog – A one-stop destination for in-depth development tutorials and new technology announcements created by students, faculty and community contributors in the NYC DCA network.

Frequency — About five posts per week

Facebook Fans: 2,136

Twitter Followers: 17,1K

14. Data Science 101 – A blog on how to become a data scientist.

Frequency — About five posts per week

Facebook Fans: 15,925

Twitter Followers: 2,365

15. Data Science Dojo – It’s a revolutionary shift in data science learning. The course offers short-duration, in-person, hands-on training that will get the aspiring data scientists started with practical data science in just a week!

Frequency — About one post per month

Facebook Fans: 12,009

Twitter Followers: 4,664

The Data Science Resources will help you keep updated and gain new knowledge and insights in the ever-evolving field of data science. The data science course at the Data Science Learning Center – Imarticus Learning will ensure updated knowledge to candidates.