At a recent training for fresher hire as part of an MNC’s analytics training program, my colleague Dr. Chetana highlighted that only 10% of the hires were women. TrustRadius reported that in 2021, 72% of women in tech are outnumbered by men in business meetings by at least a 2:1 ratio. Women are less than 1/3rd of the employees in many tech companies. As per the latest data by catalyst, women are only 5.8% of the CEO pool in the S&P 500 companies in the US! Women constitute a low percentage of student intake in most of our premier engineering colleges. A quick check of the top 10 engineering colleges in India based on NIRF ranking shows a similar low female participation.     Rank Engineering College Student Strength in 4-year UG Program No. of Female Students % of Female Students 1 Indian Institute of Technology Madras                        1,814                      272  15.0% 2 Indian Institute of Technology Delhi                        2,856                      345  12.1% 3 Indian Institute of Technology Bombay                        2,795                      307  11.0% 4 Indian Institute of Technology Kanpur                        3,184                      303  9.5% 5 Indian Institute of Technology Kharagpur                        2,251                      224  10.0% 6 Indian Institute of Technology Roorkee                        3,123                      321  10.3% 7 Indian Institute of Technology Guwahati                        2,470                      267  10.8% 8 Indian Institute of Technology Hyderabad                            969                      186  19.2% 9 National Institute of Technology Tiruchirappalli                        2,719                      662  24.3% 10 Indian Institute of Technology Indore                           798                        88  11.0% Top 5 Engineering colleges as per NIRF                     12,900                    1,451  11.2% Top 10 Engineering colleges as per NIRF                     22,979                    2,975  12.9% Data Source: NIRF Not that it was always the case! Women were the pioneers in computing and made significant contributions to the field. History is written by the winners, or, in this case, predominantly males, and much of this contribution was left unattributed for decades. In an earlier article, I had written about why it’s critical to have more diversity in AI. Lower female participation in AI has unintended negative consequences to making the future more biased. India has among the lowest female workforce participation in the world! According to World Bank data, a mere 20.79% of the workforce (for ages 15+) in India is female, compares to 47.29% worldwide. Alarmingly, this percentage has been falling since 2005, and initial reports by agencies including Oxfam and TrustRadius suggest the COVID-19 pandemic has further negatively impacted this trend in India. Image source: World Bank Besides low participation, women also struggle with salary disparity, often being paid 20% lower for equivalent work. This is compounded by the unequal workload distribution at home as well as the role as the primary caregiver. The challenges are more difficult for women looking to return from a career break – for multiple reasons, including marriage, maternity, spouse travel to a forging country where they did not have a work visa, taking care of elderly family members. Over the last few months, as hiring has resumed post-COVID, it has been heartening to see many companies ask us for focused hiring of women to improve the demographic mix of employees. We think it’s a step in the right direction to have a more balanced workforce. Returning to work has been a difficult transition for many. And, experiences with many of our female students had reinforced the message that it is unlikely to be smooth. Here are a few things that can help make the process less difficult: 1. Ask your network for help Many people make the process of getting back to work personal and do not involve the vast networks that know and trust them. Reach out and make it known you are back in the job market. The kindness of strangers and your network often springs a pleasant surprise! 2. Don’t just apply to jobs; Converse with stakeholders  Use professional networks like LinkedIn not just to apply to jobs but to connect with people in the prospective company. Understand the work culture, check your fit and possibly even get a job referral. Reaching out to hiring managers and recruitment personnel in your target companies helps bring you to their attention.  3. Apply at the companies that have getting back to work webpages Many companies have specific web pages for women returning to work. I have listed out some of them to help you get an idea.  Tata Group – Second Careers, Inspiring Possibilities IBM - Tech Re-entry Program Accenture - Career Reboot Hindustan Unilever - Career by Choice Goldman Sachs India Returnship Program It often makes sense to apply through this channel to improve your chances of being interviewed. 4. Build skills and get certifications in areas of interest Look at re-skilling yourself into an area of interest. Thanks to MOOCs and the booming EdTech sector, there are plentiful ways to gain more skills and certifications in areas of interest. Add them to your resume to make you stand out. Also, add projects, and any other work done by you, to help to distinguish you from the applicant pool. 5. Keep the faith!  A job hunt is a lonely and often saddening experience. No reverts after multiple applications, interviews that did not go well, offers that are put on hold due to any small reason may make the process difficult and depressing. Keep the faith. Your job may be just one application away! Overall, it is essential to have a pathway for everyone to seek meaningful employment. It is heartening to see many companies taking the step to help women return to the workforce and reinvent their careers. And it is essential to have more ladies back to work for the nation’s prosperity and make the workplace a more equitable and fairer place!  Remember, a setback is but a setup for a great comeback. So, don’t lose hope and continue your job hunt! Good luck!

Artificial Intelligence is the new electricity, powering the technological revolution just like electricity enabled, believes Coursera co-founder Andrew. However, AI has a significant gender and racial bias. MIT discusses how computer vision is great at recognizing light-skinned males but not good at recognizing darker females. The ability of computer vision algorithms to recognize dark-skinned females is 20%- 34% poorer than its ability to recognize light-skinned males. Research by the University of Colorado Boulder highlights the difficulty in identifying transwomen and transmen.  In the research paper, Diversity in Faces by IBM Research AI, the authors highlight that most computer vision training datasets are predominantly focused on light-skinned males. Light-skinned people constitute between 80% to 95% of the images in most training databases. The datasets are also predominantly male. Historically as well, camera manufacturers have focused on light-skinned people and paid less emphasis on capturing other skin tones appropriately.  These results in computer vision algorithms inappropriately classify a throwback image of the former First Lady of the US (FLOTUS) as “a young man wearing a black shirt”! Why? A mere 2.5% of Google employees are Black, as per its 2018 report! Women are also under-represented, comprising around 20% of the workforce in big tech companies as per a report by Bloomberg.   Source: MIT, 6. SI9I. Introduction to Deep Learning Like many other spheres in life, we need more diversity in AI. We need to actively promote people from diverse and under-represented backgrounds to join and share their views on the development of AI. Otherwise, needless to say, AI will remain biased in terms of culture, race and gender. And like a recent pop song, we’ll be left complaining “Tuada Kutta Tommy Sada Kutta Kutta.” 

Introduction 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. What are Data Analyst Tools? How to choose a Data Analyst Tool? 25 Most Popular Data Analytics Tools To Know in 2021 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 there many robust and free Data analytics tools 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 2021. 3) 25 Most Popular Data Analytics Tools To Know in 2021 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, and 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 one of the tremendously popular Data analytics tools 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 makes it ideal for analytics as well. 4. Apache Storm  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. 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 Business. 7. Tableau Tableau is among the most easy-to-learn Data analytics tools that perform an effective job of slicing and dicing your data and creating 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. For quick delivery, users may combine a group of dashboards and reports into a Power BI app. The 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 is a comprehensive platform for viewing reports for both mobile and desktop applications. Spotfire also offers point-and-click tools for predictive analytics models. 15. Thoughtspot Thoughtspot is a Data analytics software that allows users to explore Data from various sources through reports and natural language searches. The 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 one of the popular free Data analytics tools 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 into 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 for organizations that want to begin quickly, an inexpensive host version is available. 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 aimed at providing Data Scientists an easy and iterative environment. It offers an interactive SQL editor and notebook environment for analysis and visualization, and collaboration tools for novice users. 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 Data model to scan Data schemas and connect tables with Data sources. Through an integrated code editor, it 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, 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 a digital cloud platform. Users leverage its wide range of analytical features, from basic visualizations to Machine Learning algorithms for deriving Data insights. 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. Spark, Storm, and other Big Data technologies are covered in the Big Data Analyst Specialization. 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 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional.  ALSO READ Important Clustering Algorithms For Data Scientists In 2021