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Tag: Data Science

What is The Difference Between Data Analysis and Data Science?

Posted on by Imarticus Learning

Following the current technological transformations within the economy, there has been an emergence of enormous career options, wherein, Data Science is the hottest. According to the Glassdoor, Data Science arose as the highest-paid area. On the other hand, there is a significant field that has been gazing attention for years, i.e., Data Analysis. Both the Data Science and Data Analysis is often confused by the individuals.

However, the terms are incredibly different in accordance with their job roles and the contribution they do to the businesses. But, are these the only factors that make these two distinct from each other? Well, to know more we need to take a look below:


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Also Read: Top 5 Data Science Trends in 2018

Data Analysis Data Science:

Data Analysis is referred to as the process of accumulating the data and then analyzing it to persuade the decision making for the business. The analysis is undertaken with a business goal and impact the strategies. Whereas, Data Science is a much broader concept where a set of tools and techniques are implied to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, etc. to drive the essential analysis of data

Skills:

The individuals misinterpret Data Analysis with Data Science, but the methodologies for both are diverse. The skillset for the two are distinct as well. The fundamental skills required for Data Analysis are Data Visualisation, HIVE, and PIG, Communication Skills, Mathematics, In-Depth understanding of R and Python and Statistics. On the other hand, the Data Science embed the skills like – Machine Learning, Analytical Skills, Database Coding, SAS/R, understanding of Bayesian Networks and Hive

 

Techniques:

Though the areas – Data Analysis and Data Science, are often confused about being similar, but the methodology is different for both. The methods used in the two are diverse. The essential techniques used in Data Analysis are – Data Mining, Regression, Network Analysis, Simulation, Time Series Analysis, Genetic Algorithms and so on. While, the Data Science involves – Split Testing, categorizing the issues, cluster analysis and so on

Aim:

Just like the areas are different, so are their goals. The Data analysis is basically about answering the questions generated, for the betterment of the businesses. While Data Science is concerned with shaping the questions followed by answering The Data science, as illustrated above, is a more profound concept


The era of Artificial Intelligence and Machine Learning is shaping the economy in a much more comprehensive aspect. The organizations are moving towards a data-driven decision-making process. The data is becoming imperative in functioning and is not limited to the Information Technology organizations.

It is soon taking over the industries like – Sports, Medicine, Hospitality, etc. Such technological advancements have led to a rise in job opportunities in the area of Data Science and Analysis. The merely significant facet which needs to be taken into consideration is the understanding of the difference between the two. Big Data is the future which is expected to lay a considerable impact on the operations of both industries and routine life.

Related Article: What a Data Scientist Could Do?

Python Developer Salary in Terms of Job Roles

Posted on by Imarticus Learning

What is Python?

The second most liked Programming language in the world, Python is one of the widely used term in the web-development world.

Who are Python Developers?

The web-developers who design and code the software applications with the help of Python language are referred to as Python Developers.

Roles and responsibilities of Python Developers

 Python Developers as Data Scientists

Major businesses in today’s world require tools and skilled people for the data-related tasks such as data collection, data cleaning and processing.

Python Programming Course with Data ScienceData Scientists are the programmers who do these tasks for the organizations. Data Scientists gather a large quantity of data and convert it into a useful form, followed by recognizing data-analytics solutions for organizational growth.

Data Scientists encourage the data-driven approach in organizations to deal with complex business problems.

 Artificial Intelligence

In AI Python Developers create and implement the required Machine Language algorithms. They analyze the success and failure of the algorithm and rank them according to their performance for future use. Training and Retraining ML algorithms is one of the key tasks performed by Python Developers.

Salaries of Python Developers in India

The changing focus of the organizations on data-driven solutions is resulting in a manifold increase in the salaries of the Python Developers. In the coming years the demand for AI-skilled people will increase, and hence the salaries.

Major IT giants like Google, YouTube, Amazon etc. are adopting Python-driven systems and hence, manifold increases in the salaries of the Python Developers.

Python Programming Course with Data scienceThe entry level salary of a Data Scientist is approximately INR 500,000 per annum (Source: payscale.com) Data scientists with 5-9 years of work experience gets +100% more salary in India. With the experience and refinement of the skill, the salary of Data scientists shows exponential growth.

 An entry-level engineer who develops the ML Algorithms earns the average salary of approximately INR 700,000 annually. With further experience and refinement of the skills, the average salary of the Data Scientist shows exponential growth.

Factors affecting the Salaries of Python Developers

Job location: Considering the increasing demand of Python Developers, not only in India, the faster salary growth is visible in UK and in US.

Location

 Approximate Average Salary in INR

Gurgaon

 700,717

Bangalore

669,787

Delhi

600,000

Mumbai

579,728

Chennai

540,131

Hyderabad

 475,000

UK Python Developer annual salary

£67,000

US Python Developer annual salary

 $117,000

Experience:

Python Developer

Approximate Salaries in INR

Entry Level Python Developer

427,293

Med-Level Python Developer

 909,818

Experienced Python Developer

 1,150,000

Refined Skills:

Mere understanding of the Python is of no use, till is integrated with the problems and solutions. How one uses the well-known Python tools define the person’s skill set, which is a determiner of the salary.

Job Role:

Python Developer

Approximate Average Salary in INR

Data Scientists

700,000

ML Engineer

 670,000

DevOps Engineer

660,000

Software Engineer

500,000

Web Developer

 300,000

 

Python Programming Course with Data ScienceShould know more interesting things about Python programming training and Python career.

 

Advanced Data Science Skills to Stay Relevant in the Post-Pandemic World!

Posted on by Imarticus Learning

The need to upskill to meet the dynamic demands of a technology-first world has been around for the past few years; it has only become more urgent in the wake of the COVID19 pandemic. The emergence of new technologies such as Artificial Intelligence, machine learning and data science has set the tone for the future.

Data Science

In the post-COVID19 world, there are a few advanced data science skills that, when added to the toolkit of a data scientist, can make or break their career.

To ensure that your core competencies are strengthened as a data scientist, you can sign up for a comprehensive data science training course that explores the following:

 

#1: Geospatial Technologies

 With more people working on data-driven decision processes, geospatial data has helped better planning and processing of the system. This knowledge has proved invaluable in tracking the COVID19 outbreak all over the world; come the near future, geospatial technology will likely be extended to other research areas as well.

Data Science

A geospatial data scientist will need to sift through vast geographic and demographic datasets that hide gold nuggets of insight across diverse research topics.

 

 

#2: Natural Language Processing (NLP)

NLP gained traction even before the pandemic reared its ugly head. That said, it is only set to increase in importance and reach in a post-pandemic world. Natural Language Processing

Most organisations often implement self-service systems, such as bots that come with multi-language optimized NLP to help solve customer problems.

Data scientists of the future must understand NLP and master it enough to help companies develop automated solutions for a better post-COVID outcome.

#3: Computer Vision

Computer vision is an artificial intelligence field which trains computers to interpret and comprehend the visual world. It uses digital images from cameras and videos as well as deep learning models to recognise and distinguish objects correctly. With the help of algorithms, computer vision is also integrated to follow up with a programmed response. In current scenarios, computer vision has proved helpful in containing the outbreak and regulating quarantines and social distancing in cities across the world. In the future, where maintaining distance might become the norm, data scientists specializing in computer vision will automatically become more hire able.

#4: Data Storytelling

With data analytics becoming a prime concern for companies across industries, the need for good data storytelling has increased. The benefit of data analysis is not just in the evidence it provides but also in how it is made meaningful and impactful. Gripping storytelling makes it easier for non-data-scientist stakeholders to understand the value of the information and the possibilities it poses.

Data presented as contextual stories, rather than isolated data points, makes individuals more likely to understand the impact, decipher patterns and make more informed decisions.

In turn, as data storytelling would help business leaders with powerful insights, it would help them better prepare for the post-pandemic world’s opportunities.

 

#5: Explainable AI

Considering that AI has reached into nearly every area of human life, companies must be able to trust computers and their decisions. This is where the need for explainable AI emerges. Until now, companies build and sourced AI models that predicted accurate insights from large data dumps. In a post-pandemic world, they may well shift to models that also provide explanations for predictions. Explainable AI is a step forward in reducing the mistrust in non-human workflows. It makes AI systems more transparent and much fairer and all-inclusive than they were earlier.

Conclusion

Advanced data science skills are crucial to the cause of innovation and growth. Advanced upskilling is an integral step for data scientists looking to become more than relevant in the coming years.

Welcome to the Data Science Club of Imarticus Learning!

Posted on by Imarticus Learning

Imarticus Learning is among the top online education providers in India. After its data science course helped many data science aspirants to build a successful career in the industry, it has now come with a new campaign i.e. the ‘Data Science Club’. This club will aim at addressing the shortage of data scientists in India. It will also help in unifying data science aspirants from all over the country & giving them a chance to interact.

Data Science CourseYou can bring the data science club to your college/university with just a simple process. They already have registered 30 colleges from locations like Delhi, Tamil Nadu, and Karnataka.

The registration process is open & you can experience a whole new aspect of data science. One can also join the data science community of Imarticus on various social media platforms.

Mission & Vision of the Data Science Club

  • To promote students across India to build a successful career in data science.
  • To address the talent gap in the data science industry & shortage of skilled data scientists in India.
  • To facilitate the exchange of ideas & information relating to data science between club members across PAN India.
  • To provide industry-oriented learning of data science involving technological advancements & tools used in the industry.

Registration Process

Generally, most of the colleges don’t even have a data science club. You could be the first to introduce a data science club at your college/university. You can visit the Imarticus website and can easily register your college/university for the data science club. You will be required to fill a google form asking for a few details like college name, address, department, designation, email address, etc. The Imarticus panel will get back to you and will inform you about further proceedings.

Benefits of Being a Club Member

This data science club will facilitate its members from various colleges across India in understanding the importance of data science. It aims at motivating aspirants for building a successful career in data science and bridging the talent gap in the current data science industry. The benefits of joining the data science club of Imarticus are as follows:

  • Students of member colleges can attend any event/competition under the data science club for free.
  • You will get to attend lectures or webinars from industry experts/professionals.
  • The members of the club will get to test themselves by participating in the national level hackathon.
  • You will get to attend data science workshops under this club. You will also get a certification from Imarticus Learning for being a part of the data science club.
  • The members of the club will also undergo the faculty development programme.
  • Eligible members/students of the club will also get full placement support from Imarticus.
  • You will get to know about the industry practices & trends by being a member of this club. You will also get to know about the right career roadmap in the data science industry.

If you want to make a transition from data science aspirant to an expert, you have to grab this wonderful opportunity which will bring you closer to the data science community in India. One can also opt for the data science course provided by Imarticus Learning to know about the data science aspects in detail.

Register for the data science club now!

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

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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!

Data Literacy Is Very Much a Life Skill– Here Are 4 Reasons Why?

Posted on by Imarticus Learning

The world is no stranger to data; in fact, in recent times, the world has found itself being bombarded by more facts and statistics than ever before. At quite the same speed, people have also been faced with fake facts, viral social media forwards with little to no truth.

Being data literate has moved from being a niche requirement to being a life skill that allows people to distinguish between fact and fiction. Data literacy is a way of exploring and understanding statistics in a manner that provides meaning and insight.

This meaning isn’t relegated only a data science career or to businesses looking for an edge over competitors. It applies to society and its interconnected systems as a whole.

To drive the point home, here are a few advantages that data literacy offers when looked at as a life skill:

Recognising the Sources of Data

Data is everywhere, especially in a world where nearly everything is digital and produces and consumes more data. There are many different ways in which data exists, including graphs, images, text, speech, video, audio and more. Recognising the different sources of data is the first step towards working with data. The sources, formats and types all have a role to play in determining the use (and potential misuse) of data, which in turn drives data literacy.

Acknowledging the Self as a Consumer and Producer of Data

The messages you send, images you post and likes you leave on social media are examples of data. So are the transactions you make and the searches you conduct on search engines such as Google and Bing. Today, nearly every single person in the world is a data producer; those sources of data are vital to value-generating processes across industries and markets.

Similarly, people are daily consumers of data even if they don’t perceive it as that. The COVID19 pandemic has brought this into the light even further– front page statistics are at the back of everyone’s mind, as are the names of containment zones and the best practices for sanitisation.

Recognize Biases and Fallacies

Data literacy gives the people more agency to call out those producing statistical data that is biased, twisted or outright incorrect. As citizens, consumers and valued members of a society, it is imperative that every individual is able to identify false promises or glossed-over issues that allow wrong-doers to continue as they were.

Data ScienceData literacy gives people the power and the evidential backing to call out those intentionally or unintentionally propagating mistruths and fallacies through awry statistics. This way, data literacy plays a pivotal part in politics, economics and ethics of a society, indeed of the world.

Improves Data Storytelling

Instead of data points presented on their own, data that is presented as descriptive stories make individuals more likely to understand the effect, decipher trends and make more educated decisions. While data storytelling is imperative to learn for those taking a data science course, it is just as important for members of all other fields to better present their arguments such that they catch eyes.

Data has never been a strictly academic factor; however, it has often been painted as complicated, invasive or unnecessary to penetrate everyday lives. Data storytelling ensures that data is taken even further out of that box and presented as actionable insights to even the average Joe Bloggs.

Conclusion

The focus on data science and literacy shouldn’t just be restricted to mathematics and algorithms but everyday applications of data in daily lives. Data understanding allows people all over the world to take more control of what they’re producing and consuming. Data fluency and literacy is achievable by all.

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.

What is Data Science

Posted on by Imarticus Learning

What is Data Science

Data science is a field with a plethora of possibilities and is evolving very quickly in our day and age. Hence since it does not have any clear cut boundaries, coming up with an exact definition for it becomes a tough task. In simple terms, data science is the process of collecting information as well as creating actionable insights from unorganised raw data. It involves taking raw data and making sense of it.

Data is something that can not be easily understood by a common individual. It depends on machines to understand and interpret it, process it and then change it into something meaningful.

Data has become completely intertwined with everything we do today and the modern world can not function without it. Various companies, countries and individuals are looking to digitise their information as fast as possible to increase efficiency. Taking up a data science course is highly recommended for people to gain more knowledge and information on the following topics.

How does Data Science work exactly?

When a person chooses to go into the field of data science they would need to meet a large number of requirements in order to be good at their job. This can be done by choosing to go through a data science course. These disciplines include being able to products a complete, thorough and clean output of the raw data that has been provided.

Other requirements include engineering, mathematics, statistical knowledge, advanced computing and creativity. This would allow the individual to search through the data in an efficient manner and organise the messy raw information in front of them. They will then need to convey only the important parts that will assist in driving innovation and efficiency. As mentioned earlier, a data science course will be of an advantage for those interested to enter the field.

Data science is heavily dependent on artificial intelligence and machine learning. AI helps in creating models and using algorithms to predict outcomes. There are five stages to data science. They are:

  • Capture: This involves the acquisition, entering and extraction of data
  • Maintain: This step involves warehousing, cleaning, staging, processing and structuring of data.
  • Process: Here data is mined, classified, modelled and summarised
  • Communicate: The data is reported and visualized. Then various decisions are made regarding the data.
  • Analyze: A qualitative and predictive analysis of the data is done.

A data science course would harbour more information in further detail, thus improving your understanding of this career path.

Where is Data Science Used?

Data science is helping us move forward in our ever-expanding world. It has helped us reach various goals and helped improve the efficiency of work. It is being used in various fields today. Some of these fields have been listed below.

  1. Self-driving cars: Using AI and machine learning, transport today has reached a whole new level. Companies like Tesla, Volkswagon and Ford have begun incorporating complex AI features into their vehicles, thus leading a range of autonomous cars. Using small cameras and tiny sensors, these cars have the ability to send information back and forth in real-time.
  2. Healthcare: Data science is being used in healthcare to a large extent today. This ranges from storing patient information in a compact and efficient manner through a database to making new breakthroughs in the fields of disease study.
  3. Cybersecurity: Data science makes it possible to source through large data sets and thus is perfect for detecting any kind of malware present. This thus makes it ideal for use in cybersecurity.

Data science is hence a very important part of our lives today. For anyone looking to work in such a feeling, it is ideal that they go through a data science course. A data science course would equip the individual with all the necessary information and tools to succeed in this particular field.

Also Read: Resources to Learn Data Science Online

Preparing for your data science interview: Common R programming, SQL and Tableau questions

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Preparing for your data science interview: Common R programming, SQL and Tableau questions

This data science interview questions blog includes the most frequently asked data science questions. Here is the list of top R programming, SQL and Tableau questions.

R Programming Interview Questions

R finds application in various use cases, from statistical analysis to predictive modelling, data visualisation and data manipulation. Facebook, Twitter and Google use R-programming training to process the huge amount of data they collect.

Which are the R packages used for data imputation?

Missing data is a challenging problem to deal with. In such cases, you can impute the lost values with plausible values. Amelia, Hmisc, missForest, Mice and mi are the data imputation packages used by R. In R, missing values are represented by NA, which should be in capital letters. 

Define clustering. Explain how hierarchical clustering is different from K-means clustering.

A cluster, just like the literal meaning of the word, is a group of similar objects. K denotes the number of centroids needed in a data set. While performing data mining, k selects random centroids and optimises the positions through iterative calculations.

The optimisation process stops when the desired number of repetitive calculations have taken place or when the centroids stabilise after successful clustering. Hierarchical clustering starts by considering every single observation in the data as a cluster.  Then it works to discover two closely placed clusters and merges them.  This process continues until all the clusters merge to form just a single cluster. 

SQL Interview Questions

If you have completed your SQL training, the following questions will give you a taste of the technical questions you may face during the interview.

What is the difference between MySQL and SQL?

Standard Query Language (SQL) is an English-based query language, while MySQL is used for database management.

What do you mean by DBMS, and how many types of DBMS are there?

DBMS or the Database Management System is a software set that interacts with the user and the database to analyse the available data. Thus, it allows the user to access the data presented in different forms – images, strings, or numbers – modify them, retrieve them and even delete them.

There are two types of DBMS:

Relational: The data is placed in some relations (tables).

Non-Relational: Random data that are not placed in any relations or attributes.

Tableau Interview Questions

Tableau is becoming popular among the leading business houses. If you have just completed your Tableau training, then the interview questions listed below could be good examples.

What is Tableau? How is Tableau different from the traditional BI tools?

Tableau is a business intelligence software connecting users to their respective data. It also helps develop and visualise interactive dashboards and facilitates dashboard sharing. Traditional BI tools work on an old data architecture supported by complex technologies. Tableau is fast and dynamic and is supported by advanced technology. It supports in-memory computing. ‘Measures’ denote the measurable values of data. These values are stored in specific tables, and each dimension is associated with a specific key. Dimensions are the attributes that define the characteristics of data. For instance, a dimension table with a product key reference can be associated with attributes such as product name, colour, size, description, etc.

The above questions are examples to help you get a feel of the technical questions generally asked during the interviews.

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.