Being someone who is truly passionate about data science and emerging technologies, I found myself seeking newer insights on tools, techniques, market trends, and more every day. This quest for knowledge appeared perpetual. That’s when I started listening to data science podcasts while doing regular tasks. It takes a solid one hour for me to get to work from where I live. I ditched the habit of listening to random songs that music players curated for me and resorted to podcasts on data science.  

This led me to discover one gem after the other, each of which has a permanent spot in my playlist. I felt it would be great to share my list with someone like you who is passionate and keen about data science as well.

So, here’s the list.

5 Essential Data Science Podcasts You Should Subscribe To 

1. Towards Data Science 

I was keen that I should start the list with this show. Towards Data Science is one of the most popular podcasts in this space, and there’s a solid reason for this. The host, Jeremie Harris, ensures his guests are highly relevant and that they bring in their distinct voices and expertise in Data Science.  

There is a flow to his conversations that keep us hooked on the topic. Besides, Jeremie also breaks down complex jargon and technical details in the middle of conversations to ensure we, as listeners, don’t disconnect at any given point in time. This gives us a more holistic understanding of the topic under discussion. If you’re looking for solid takeaways on Data Science, this is your go-to podcast. 

2. Data Skeptic 

Firstly, this podcast boasts of an incredible library with over 370 episodes curated and compiled as mini and regular episodes. One of the reasons for this abundant repository is the fact that this show has been on air since 2014. That’s also the time when the data revolution happened. The seasons are classified to mark the diverse aspects of data science, such as K-means, ad-tech, time series, consensus, NLPs, and more.  

Kyle Polich is the host of the podcast, and she has given us a unique perspective on Data Science by discussing fake news as well. Binge-worthy! 

3. Data Stories

Data is of no use if you can’t infer insights and takeaways from it. And how does one do it? Through data visualization. Storytelling is an integral aspect of data science, and this is done effectively through data visualization. While most of the podcasts talk about key and regular topics in data science, Data Stories discusses the visualization part of it.  

The hosts, Moritz Stefaner and Enrico Bertini, extensively talk about data analysis and visualization topics and curate airtight questions for their guests. This ensures solid responses that are enlightening. If you intend to understand how data practically influences our lives on a daily basis, tune into this podcast whenever you can.  

4. SuperDataScience 

As the name suggests, this is one super series on everything data science. From tools and techniques to processes and career paths in data science, the host Kirill Eremenko does a spectacular job of curating topics and explaining them. The best part about the series is not just your interviews. It’s the motivating stories and bytes on the topic that help you improve as a data scientist.  

5. Lex Fridman Podcast 

If wisdom had an online address, it would be the Lex Fridman Podcast. His episodes will take you on a guilt trip once you realize that so much knowledge and wisdom is available for free. What started as the AI Podcast evolved into an all-encompassing series where the very nature of consciousness, philosophy, and even abstract concepts are discussed with guests. From singularity and superintelligence to tech and astrophysics, there is abundant knowledge for you.  

Final Thoughts 

These podcasts have been my most ideal companions during my commute, lunch, and runs. They have given me tremendous insights on not just the topics but how it overlaps into diverse yet intricate aspects of our everyday lives.  

Start listening to them if you haven’t already. Also, list down your recommendations. Would love to update my playlist.  

The post 5 Podcasts On Data Science That Found A Permanent Place In My Playlist appeared first on Jigsaw Academy.

Here is a list of the 10 most popular analytic tools used in the business world.

COMMERCIAL SOFTWARE

1. 1. MS Excel: Almost every business user has access to MS Office suite and Excel. Excel is an excellent reporting and dashboarding tool. For most business projects, even if you run the heavy statistical analysis on different software but you will still end up using Excel for the reporting and presentation of results. While most people are aware of its excellent reporting and graphing abilities, excel can be a powerful analytic tool in the hands of an experienced user. Latest versions of Excel can handle tables with up to 1 million rows making it a powerful yet versatile tool.
   2. SAS: SAS is the 5000-pound gorilla of the analytics world and claims to be the largest independent vendor in the business intelligence market. It is the most commonly used software in the Indian analytics market despite its monopolistic pricing. SAS software has wide-ranging capabilities from data management to advanced analytics.

1. SPSS Modeler (Clementine): SPSS Modeler is a data mining software tool by SPSS Inc., an IBM company. It was originally named SPSS Clementine. This tool has an intuitive GUI and its point-and-click modelling capabilities are very comprehensive.
2. Statistica: is a statistics and analytics software package developed by StatSoft. It provides data analysis, data management, data mining, and data visualization procedures. Statistica supports a wide variety of analytic techniques and is capable of meeting most needs of the business users. The GUI is not the most user-friendly and it may take a little more time to learn than some tools but it is a competitively priced product that is value for money.
3. Salford systems: provides a host of predictive analytics and data mining tools for businesses. The company specialises in classification and regression tree algorithms. Its MARS algorithm was originally developed by world-renowned Stanford statistician and physicist, Jerome Friedman. The software is easy to use and learn.
4. KXEN: is one of the few companies that is driving automated analytics. Their products, largely based on algorithms developed by the Russian mathematician Vladimir Vapnik, are easy to use, fast and can work with large amounts of data. Some users may not like the fact that KXEN works like a ‘black box’ and in most cases, it is difficult to understand and explain the results.
5. Angoss: Like Salford systems, Angoss has developed its products around classification and regression decision tree algorithms. The advantage of this is that the tools are easy to learn and use, and the results easy to understand and explain. The GUI is very user friendly and a lot of features have been added over the years to make this a powerful tool.
6. MATLAB: is a statistical computing software developed by MathWorks, MATLAB allows matrix manipulations, plotting of functions and data, implementation of algorithms and creation of user interfaces. There are many add-on toolboxes that extend MATLAB to specific areas of functionality, such as statistics, finance, image processing, bioinformatics, etc. Matlab is not a free software. However, there are clones like Octave and Scilab which are free and have similar functionality.

OPEN SOURCE SOFTWARE

1. R: R is a programming language and software environment for statistical computing and graphics. The R language is an open source tool and is widely used by the academia. For business users, the programming language does represent a hurdle. However, there are many GUIs available that can sit on R and enhance its user-friendliness.
2. Weka: Weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software, developed at the University of Waikato, New Zealand. Weka, along with R, is amongst the most popular open source software used by the business community. The software is written in the Java language and contains a GUI for interacting with data files and producing visual results and graphs.

UPDATE 1

World Programming system recently launched its version 3. The version has a lot of improvements that take it very close to Base SAS. This tool is rapidly becoming popular with expert analysts who are looking for cheaper options to SAS. Search this blog for the term “WPS” for more interesting information on this exciting tool.

UPDATE 2- JULY 2015 BY GUNNVANT SINGH

Organizations use a variety of products to extract insights from the data. The complete spectrum of the products ranges from simple tools like excel to number crunching mammoths like SAS or R.

Let us take a look at some other popular data analytics tools today, not mentioned in the lists above.

1. SQL: It is the primary data storage tool across all organizations. It is still being used to manipulate data and produce reports. Anyone who wishes to be in the analytics industry needs to know SQL.
2. Tableau: Tableau as a tool has been adopted by most analytics companies. Its visualization capabilities are well recognized in the industry. It is used mostly to produce visualizations and reports. It also allows users to explore the data before beginning the task of predictive modelling.
3. Orange: It uses the concept of visual programming and simplifies the task of predictive modelling. All major predictive algorithms are built into the software. One can also add functionalities by writing scripts in python. The fact that it is open source makes it all the more attractive.
4. KNIME: The company behind KNIME provides a suite of products catering to different needs. It also like orange makes use of visual programming.
5. Azure ML: This is one product from Microsoft that is poised to be the next big thing. This product is actually an ecosystem that gives users the ability to create data products by integrating machine learning module (mostly R libraries) with a robust backend and a pretty frontend. The data connections can be made from different sources, SQL servers, Hadoop clusters.

Click here to read more about analytics courses.

Click here to read the Beginner’s guide to analytics.

RELATED ARTICLES:

• Key Skills for a Successful Analytics Career
• Career in Analytics in a KPO
• Educating on Analytics
• 42 – the answer to everything!
• Books on Analytics

Want to also refresh yourself with the terms that get thrown around in the field of analytics? Take a look at the article Analytics Terminology by Gaurav Vohra, Co Founder and CEO of Jigsaw Academy and  find out all you need to know about those big and small analytics terms you’ve heard but not sure what they really mean

The post 10 most popular analytic tools in business – Updated 2015 appeared first on Jigsaw Academy.

Last night at 9 pm Congress Vice President Rahul Gandhi sat down with Interview Tyrant, Times Now Editor in Chief Arnab Goswami, for what was his first ever one on one interview. Team Jigsaw has analysed the approximately 7500 words that Rahul Gandhi uttered in that one and a half hour and here’s what we found:

• He brought up the word system 70 times
• He used the word people 66 times
• People, party and  country were used more than 40 times each.
• He brought up Gujarat 20 times (BTW this was the only State brought up).

In the table on the right we have listed the words that Rahul Gandhi used most. If we negate the first two on the list, we see that the interview seemed to centre around the theme of people vs the system. Rahul addressed the issues of the system; acknowledged that people get hurt and that they are not heard. He said that he wants to change this system and open it up, make it transparent. Essentially he seemed to be saying the fight is between the people and the system.

[leadsquared-form id=”12825″]

However nowhere was the term ‘system’ defined and so we are left to assume that the system is indeed the government, the politicians, the bureaucracy and the corruption. Isn’t this ironic, since the Congress has been in power for the last 10 years and so naturally make up this ‘system’.

Nevertheless Mr Gandhi comes out sounding passionate about the need to change the system from within. The RTI was talked about quite extensively and Rahul said that he was pro all forms of openness. He said that he wanted to transform the system, to bring in youngsters and make sure they are empowered.

Social media has since been buzzing with opinions and comments. Did Rahul fare well? Has brand Rahul come out for the better or for the worse? Each to his or her own opinion, but one thing for sure, kudos to the man for facing Arnab and coming out alive!

The post An Analysis of Words- The Rahul Gandhi Interview appeared first on Jigsaw Academy.

In this article, I will talk about 3 ways in which one can transform the variables before including them in a model.

1. Let’s assume we have a telecom churn dataset. In this dataset, we have a variable that captures the state to which a customer belongs (Categorical Variable). For simplicity, there are 3 states in my dataset – Bihar, Orissa and Gujarat. Let’s say I create a new variable and assign numeric values – 1, 2 and 3 to Bihar, Orissa and Gujarat. I then plug in this variable into my logistic model that predicts churn and I end up getting a single coefficient (For Eg. 1.5) for state.

This is how I would interpret this coefficient: If all the variables have the same values for 2 customers and the only difference is the state they are from, then the person from Orissa has a higher chance of churning than a person from Bihar as much as a person from Gujarat has over a person from Orissa.

In reality, this may not always be right. It might be that the increase is not always constant. The increase in churning probability might be higher from Bihar to Orissa and a little lower from Orissa to Gujarat.

In order to account for these differences, it is advisable to create dummy variables for the different levels of the categorical variables.  This way, we can get individual coefficients for individual states.

2. Even for continuous variables, it might be that the single coefficient generated may not always be significant, even though one knows that the variable will definitely be a very good predictor.

There are 2 alternatives to this:

• One will be to group different ranges of the continuous variables into different levels, make that variable categorical in some sense and then plug this categorical variable into the model.

• Other option will be to create dummies for the categorical variable created in the first option and use them in the model.

3. There is a third, very interesting transformation that one can think of. In the data exploration stage, when we see that a particular variable has a non – linear relationship with the dependent variable, in order to linearize this relationship, the independent variable should be treated appropriately. The treatment can be squaring/cubing the independent variable, log transformation etc. Eg: We will create a squared term for diminishing returns.

Thus, before any modeling can begin a lot of time has to be spent on data preparation. Also remember that data preparation is not a one-time process, it is iterative. Sometimes, even after building the model, certain transformations might have to be done. For example, if there is heteroscedasticity, log transformation on the dependent variable might be appropriate. So, don’t rush. Your modelling will only be accurate if you have prepared your data well and have made required transformations along the way.

The post 3 Ways to Transform Your Variables Before Modeling appeared first on Jigsaw Academy.

Base SAS certification officially known as “The SAS Certified Base Programmer for SAS 9 credential” is a certification for beginners in SAS programming. This certification requires knowledge and experience of SAS ver 9.3 and is one of the essential requirements for many other advanced SAS certifications.

The certification tests the participants’ knowledge on these broad topics

• Getting data in and out of SAS
• manipulate and transform data
• combine SAS data sets
• create summary reports using SAS procedures
• identify and correct data, syntax and programming logic errors.

To earn this credential, you must pass the SAS Base Programming for SAS 9 exam.

Is Base SAS certification useful?

Base SAS certification is usually considered helpful in securing an entry level job in analytics. However, there is debate over how much useful it really is. The certification is a decent assessment of a person’s SAS programming skills. However, it does not cover a number of non-SAS related skills that are essential for a business analyst. Here is a link to an interesting debate on Stack Overflow.

How do I prepare for Base SAS certification?

If you are an experienced SAS analyst and want to get a feel of the kind of questions asked in the test, you can explore the SAS website (http://support.sas.com/certify/samples.html). There are only 5-6 questions for each certification so it is not a comprehensive list but it gives you an idea of what to expect in the exam.

If you need more preparation for the exam, the easiest (but very expensive) option is to take one of the many training programs offered by the SAS Institute or one of its certified partners.

A 2 week SAS training boot-camp by the SAS institute costs upwards of Rs. 1 lakh. It includes one complementary attempt of Base SAS Certification Exam (worth Rs. 9,000/-)
You can also buy one of the many books available from the SAS Institute that can help you prepare for the exam. But given the nature of the exam, learning by books only may not be the best option.

There are many online resources that can aid one’s preparation for the exam. One of them is http://sascert.blogspot.in/

How do I enrol for the Base SAS certification?

You can register online for the exam – http://support.sas.com/certify/creds/bp.html#t2

You can also register for the exam by calling this number – +91-124-4517160 (Monday – Friday, 9:00 to 17:30).
The exam fee is $180.

Will Jigsaw Academy’s Foundation course help me prepare for Base SAS certification?

Jigsaw Academy’s foundation course is designed to prepare people for the role of a business analyst. The course covers a number of things other than the SAS language and the curriculum is much broader than what is required for the SAS certification. However, the course covers many of the topics that fall under the base SAS certification syllabus. Many of our students have successfully obtained the Base SAS certification after completion of the Foundation course.

Read about our Foundation course.

The post Base SAS Certification appeared first on Jigsaw Academy.

The site has a collection of analytics articles, case studies, news and events. There is a separate section featuring companies in analytics.

The site is a welcome addition to the list of online resources on analytics. You can check it out here.

The post Analytics India Magazine appeared first on Jigsaw Academy.

“This is awesome. Fascinating work! I always knew this intuitively but you gave it the statistical backbone. Superb work!”
“OH NO. STOP THIS MEANING LESS STATISTICS”
“perfect analysis bro…keep it up.”
“Absolutely rubbish analysis… “

Full marks to you if you guessed it. Yes its about Cricket.

Nothing gets Indians excited like Cricket. I recently wrote an article about who is the best ODI batsman for India. The analysis is fairly simple. I compared India’s top 3 all-time scorers i.e. Sachin Tendulkar, Rahul Dravid and Sourav Ganguly.

I built a model to calculate the impact of each run scored by these 3 batsmen on the team’s chances of winning. I then used it to calculate the average impact per inning. It turns out that in every inning that Dada has batted for India, he has improved the team’s chances of winning by 13% on average. This can be called Dada’s contribution from his batting in every inning.

Similarly, Dravid’s contribution is 11% and Sachin’s contribution is 10%.

Thus it turns out that Dada has had a greater impact on the team’s win rate than both Sachin and Dravid.

As an analyst, I know that this is just one way of looking at things. For example, if instead of using averages, we use the totals, then we can calculate the life time contribution of each batsman. (We have calculated average contribution per inning. we can also calculate the total lifetime contribution.)

There are dozens of other variables that can be added to make this analysis more robust. Is a victory against Australia or Pakistan more valuable than a victory against Bangladesh. If it is, then we need to add this information to the model. As I said, this is a very simple 1-variable regression analysis.

Read the full article here. Some more responses here.

The post An Application of Logistic Regression appeared first on Jigsaw Academy.

Here is a brief profile of Ajay, taken from one of his recent interviews.

“Ajay Ohri has been working in the field of analytics since 2004 , when it was a still nascent emerging Industries in India. He has worked with the top two Indian outsourcers listed on NYSE,and with Citigroup on cross sell analytics where he helped sell an extra 50000 credit cards by cross sell analytics .He was one of the very first independent data mining consultants in India working on analytics products and domestic Indian market analytics .He regularly writes on analytics topics on his web site www.decisionstats.com and is currently working on open source analytical tools like R and newer softwares like WPS.”

Ajay Ohri’s interview with Vincent Granville

The post An interesting blog on analytics appeared first on Jigsaw Academy.

Data Analysis is the technique by which raw data is transformed into useful statistics, insights, and explanations to make data-driven business decisions. Data Analysis has become the cornerstone of modern business operations. It is a daunting task to choose the best Data analytics tool since no tool fits every need. Let’s look at the key factors for choosing between the Data analytics tools and then explore some of the most popular Data analytics tools available in the market today.

Let’s start with looking at a few basic questions that will help you choose the best Data analytics tools.

1. What are Data Analyst Tools?
2. How to choose a Data Analyst Tool?
3. 28 Most Popular Data Analytics Tools To Know in 2022

1) What are Data Analyst Tools?

The term ‘Data analytics tools’ is used to classify software and applications used by Data Analysts to create and execute analytic processes that help businesses make smarter, more informed business decisions while minimizing cost and boosting profits.

2) How to choose a Data Analyst Tool?

How do you find one amongst several Data analytics tools that’s a good fit for your company? Start by considering your company’s business requirements and learning who will be using the Data analytics tools. Will it be used by seasoned Data Analysts and Data Scientists or non-technical users who need an intuitive interface? Some Data analytics tools provide an immersive experience in code creation, generally with SQL, while others are more concerned with click-and-point review best suited for freshers. The Data analytics software should also offer support for visualizations relevant to your business goals.

Consider the ability of Data analytics software to model data. Some support a syntactic and semantic layer or can perform data modeling themselves. If you do not wish to use one that does, you’ll have to use SQL or Data analytics tools like the data build tool (dbt) to model your data before analysis.

Finally, take price and licensing into consideration. Some Data analytics tools charge license or subscription fees, while some Data analytics tools are free. The most expensive Data analytics tools are not always the most comprehensive, and many robust and free Data analytics tools are available in the market that shouldn’t be overlooked.

Now that we have covered what are Data analytics tools and how to choose the best Data analytics software for your business. Let’s explore the popular Data analytics tools of 2022.

3) 28 Most Popular Data Analytics Tools To Know in 2022

To make the most out of the infinite number of Data analytics tools currently offered on the market, we will explore the 25 most prominent Data analytics tools needed to be an expert Data Analyst.

1. R

R is now one of the most popular analytics tools in the industry. It has surpassed SAS in usage and is now the Data analytics tool of choice, even for companies that can easily afford SAS. Over the years, R has become a lot more robust. It handles large data sets much better than it used to, say even a decade earlier. It has also become a lot more versatile.

1800 new packages were introduced in R between April 2015 and April 2016. The total number of R packages is now over 8000. There are some concerns about the sheer number of packages, but this has certainly added a lot to R’s capabilities. R also integrates very well with many Big Data platforms, which have contributed to its success.

2. Python

Python has been one of the favorite languages of programmers since its inception. The main reason for its fame is the fact that it’s an easy-to-learn language that is also quite fast. However, it developed into one of the powerful Data analytics tools with the development of analytical and statistical libraries like NumPy, SciPy, etc. Today, it offers comprehensive coverage of statistical and mathematical functions.

Increasingly, we are seeing programmers and other tech folks moving into analytics. Most of these guys are already familiar with Python; therefore, it has become a Data analytics tool of choice for many data scientists.

3. Apache Spark

Spark is another open-source processing engine that is built with a focus on analytics, especially on unstructured data or huge volumes of data. Spark has become a tremendously popular Data analytics tool in the last couple of years. This is because of various reasons – easy integration with the Hadoop ecosystem being one of them. Spark has its own machine-learning library, which also makes it ideal for analytics.

4. Apache Storm

 The storm is the Big Data tool of choice for moving data or when the data comes in as a continuous stream. Spark works on static data. The storm is ideal for real-time analytics or stream processing.

5. PIG and HIVE

Pig and Hive are integral Data analytics tools in the Hadoop ecosystem that reduce the complexity of writing MapReduce queries. Both these languages are like SQL (Hive more so than Pig). Most companies that work with Big Data and leverage the Hadoop platform use Pig and/or Hive.

6. SAS

 SAS continues to be one of the widely used Data analytics tools in the industry. Some flexibility on pricing from the SAS Institute has helped its cause. SAS continues to be a robust, versatile, and easy-to-learn tool. SAS has added tons of new modules. Some of the specialized modules that have been added in the recent past are – SAS Analytics for IoT, SAS Anti-money Laundering, and SAS Analytics Pro for Midsize Businesses.

7. Tableau

Tableau is among the most easy-to-learn Data analytics tools that effectively slice and dice your data and create great visualizations and dashboards. Tableau can create better visualizations than Excel and can most definitely handle much more data than Excel can. If you want interactivity in your plots, then Tableau is surely the way to go.

8. Excel

Excel is, of course, the most widely used Data analytics software in the world. Whether you are an expert in R or Tableau, you will still use Excel for the grunt work. Non-analytics professionals will usually not have access to tools like SAS or R on their systems. But everyone has Excel. Excel becomes vital when the analytics team interfaces with the business steam.

9. QlikView

Qlikview and Tableau are essentially vying for the top spot amongst the data visualization giants. Qlikview is supposed to be slightly faster than Tableau and gives experienced users a bit more flexibility. Tableau has a more intuitive GUI and is easier to learn.

10. Splunk

 Splunk is more popular than some of the more known Data analytics tools like Cloudera and Hortonworks. It started as a ‘Google for log files, which means its primary use was to process machine log files data. It has now become much more than that. Splunk has great visualization options, and a web interface makes it easy to use.

11. Microsoft Power BI

Microsoft Power BI is a top business intelligence platform that offers support for dozens of data sources. This Data analytics software allows users to create reports, displays, and dashboards and post them. Users may combine a group of dashboards and reports into a Power BI app for quick delivery. Power BI helps users create and implement automatic models by applying Machine Learning with Azure Machine Learning.

12. SAP BusinessObjects

SAP BusinessObjects provides a suite of Data analytics tools for data discovery, analysis, and reporting. The tools are designed for novice technical users but also for carrying out complex analyses. BusinessObjects incorporates Microsoft Office products, enabling Business Analysts to easily reverse and switch between applications like Excel and reports from BusinessObjects. It also enables self-service predictive analytics.

13. Sisense

Sisense is a Data analytics software aimed at aiding both technical developers and the Business Analytics process and visualizing all of their business data. It offers a wide variety of drag-and-drop software and interactive dashboards for collaboration. The Sisense platform’s unique feature is its custom in-chip technology, which optimizes calculation to utilize CPU caching instead of slower RAM. This can lead to 10-100 times faster computation for certain workflows.

14. TIBCO Spotfire

TIBCO Spotfire is a Data analytics software that provides natural language search and AI-powered data insights. This comprehensive platform for viewing reports for mobile and desktop applications. Spotfire also offers point-and-click tools for predictive analytics models.

15. Thought spot

Thoughtspot is a Data analytics software that allows users to explore data from various sources through reports and natural language searches. SpotIQ, its AI-powered system, automatically seeks insights to help users discover trends they didn’t know to search. It also enables users to automatically link tables from various Data sources to break down Data silos.

16. Google Data Studio

Google Data Studio is a popular free Data analytics tool for dashboarding and data visualization that automatically integrates with most other Google applications, such as Google Analytics, Google Ads, and Google BigQuery. Data Studio is perfect for those who need to evaluate their Google data due to its convergence with other Google services. For example, marketers could create dashboards to help analyze consumer conversion and retention for their Google Advertising and Analytics results. Data Studio can run with Data from several other sources as long as the Data is replicated first to BigQuery using a Data pipeline such as Stitch.

17. Grafana

Grafana is another free, open-source Data analytics software for monitoring and observing metrics across diverse databases and applications. It offers a real-time view of external processes and warns users when such incidents occur. Grafana is widely used for tracking their applications by tech and DevOps engineers.

18. Redash

Redash is a lightweight and cost-effective Data analytics software for querying data sources and building visualizations. The code is open source, and an inexpensive host version is available for organizations that want to begin quickly. Redash’s heart is a query editor which offers a quick interface for requests, schemes, and integration management. Search results are cached in Redash, and users can automatically schedule updates.

19. Jupyter Notebook

Jupyter Notebook is one of the robust free, open-source online Data analytics tools that can be administered in a browser after installation using the Anaconda platform or Python’s package manager, pip. It enables developers to generate reports with Live Code Data and views. This Data analytics software supports more than 40 programming languages. Formerly known as IPython Notebook, Jupyter Notebook was initially developed using Python. It enables developers to make use of Python’s wide variety of analytics and visualization packages. The tool has a large group of users who also use other languages.

20. IBM Cognos

IBM Cognos is a Data analytics software for business intelligence with built-in AI tools to show and clarify information concealed in plain English. It has automated Data preparation software to automatically clean and aggregate data sources, enabling the fast integration and analysis of data sources.

21. Mode

Mode is a Data analytics software that provides data scientists with an easy and iterative environment. It offers an interactive SQL editor, notebook environment for analysis and visualization, and collaboration tools for novice users. The mode has a unique Helix Data engine that streams and stores Data from external databases to allow swift and interactive analysis. The Data Analysis supports up to ten GB of data in memory.

22. KNIME

KNIME is the abbreviation for the Konstanz Information Miner and is a free, open-source Data analytics software that supports Data integration, processing, visualization, and reporting. It integrates Machine Learning and Data mining libraries with minimal or no programming requirements. KNIME is excellent for Data Scientists who do not inherently have proficient programming skills and need to incorporate & process Data for building Machine Learning and other statistical models. Its graphical interface facilitates point-and-click analysis and modeling.

23. Looker

Looker is one of the cloud-based business intelligence and data analytics tools. It automatically generates a Data model to scan Data schemas and connect tables with Data sources. An integrated code editor allows Data engineers to modify the created models.

24. RapidMiner

RapidMiner is a Data analytics software that caters to all the technology users need, from integration, and cleaning to Data transformation before they run predictive analytics and build statistical models. Nearly all this is done by the users through a simple graphical interface. RapidMiner can also be expanded by using R and Python and various third-party plugins available on the organization’s marketplace.

25. Oracle Analytics Cloud

Oracle Analytics Cloud is another suite of Cloud-based business intelligence and Data analytics tools. It focuses on helping big corporations to transform their legacy systems into digital cloud platforms. Users leverage its wide range of analytical features, from basic visualizations to Machine Learning algorithms for deriving Data insights.

26. QuickSight 

QuickSight is Amazon’s business intelligence and analytics software. It’s a cloud service that connects to cloud Data sources, including AWS, SaaS, Excel, and others. QuickSight’s goal is to empower decision-makers to study and comprehend Data in a simple and visual manner. However, it has advanced capabilities; for example, it can be used for Machine Learning. It, like Power BI, enables the sharing of analyses and reports and collaborative analysis. 

27. Sequentum Enterprise 

Assume you require a sophisticated Data Extraction tool for web crawling. Sequentum Enterprise is an ideal tool for developing, testing, and producing large-scale web data extractions in such a situation. Enterprise was created for businesses that rely heavily on organized online data and legal compliance. Users can use C# or VB.NET to control and debug the crawler, or they can write scripts. Sequentum Enterprise includes sophisticated capabilities not seen in other solutions, such as tracking, Data Extraction Success Criteria, Legal Compliance, and Production Failover. However, this Data analysis tool may be a bit intimidating for people familiar with the foundations of Data analysis and programming. If you’re just starting, you might want to avoid utilizing Sequentum Enterprise until you’ve gained some work experience. 

28. Datapine 

Datapine provides both novice and professional users with simple yet sophisticated analytical features. This well-known business intelligence application has a drag-and-drop interface, sophisticated predictive analysis capabilities, and interactive dashboards and charts. Moreover, advanced users can create their own queries using the advanced SQL option. Datapine’s distinguishing features are its speed and simplicity. 

Conclusion

This listicle contains the new and updated Data analytics tools list of the most popular tools in the analytics industry. If you are interested in learning these tools, here is what you need to do. To learn SAS, R, and Python, head to our Data Scientist Specialization. The Big Data Analyst Specialization covers Spark, Storm, and other Big Data technologies. We have a course on Analytics with Excel to help you take your step into analytics.

If you are interested in making a career in the Data Science domain, our 9-month online PG Certificate Program in Data Science and Machine Learning can help you immensely in becoming proficient in the nuances of Data Science tools and technologies. 

ALSO, READ

• Important Clustering Algorithms For Data Scientists In 2021

The post Top 28 Data Analytics Tools For Data Analysts (2022-23) | UNext Jigsaw appeared first on Jigsaw Academy.

IIT Bombay has a 1 year course in advanced analytics. This course is offered in a distance learning format in collaboration with Hughes Technologies. Eligibility for this course is a minimum of 50% marks in Graduation.

IIM Calcutta offers a similar course (also in collaboration with Hughes). This is again a distance learning course with classes conducted through video conferences. Graduates with minimum 50% marks and 2 years of work experience may apply. The IIM Calcutta course has strong elements of finance and business management in the course which may not strictly come under business analytics.

MICA Is another MBA institute that offers course in market research and data analytics. This program focuses on building a strong foundation in Market Research & Data Analytics for entry-level to mid-level professionals.

IIM Bangalore’s 1 year course is relatively newer but is fast becoming popular in the field of analytics.The course consists of six modules and a project.  The duration of each module is 5 days.  In addition there is an optional module on, “Applied Analytics using SAS Enterprise Miner” which is carried out by SAS Institute’s analytical consultants and is mapped on to the international predictive modeling certification using SAS enterprise miner.

Here are the eligibility criteria for the IIM B course –

The participants should have a Bachelor’s degree in engineering/science/commerce or arts with mathematics as one of the subjects during their Bachelor’s programme. The participants of Executive Education are expected to have at least 5 years of work experience.  For profiles with exceptional qualifications, the experience criteria may be waived.

The reviews of the IIM courses as well as the IIT Bombay course are similar. The courses cover a wide variety of topics and of course come with strong brand names. But they struggle to bridge the gap between theory and practical. The exposure to real life case studies, real business data sets and real business problems is not as rich as it could be. Here is a review of the course by one of the IIT Bombay alumnus. http://himanshumanroa.blogspot.in/2012/05/is-advanced-analytics-course-from-iit.html

For those not satisfied with what is available in India, the North Carolina State university offers a 1 year course in advanced analytics in collaboration with the SAS institute. This course is obviously focused heavily on the SAS Institute’s tools and many of the students get hired by SAS at the end of the course.

There are many new offerings that are coming out every year. This is a round-up of some of the established analytics courses in India and abroad. I hope you find it useful.

The post Analytics Training – Courses in India and abroad appeared first on Jigsaw Academy.