First of all Many thanks to the team for the revamp of the reporting tools. It's so nice to see it's getting a bit more dynamic.
- I'd love to have more options extended to the graph, such as being able to add 3 dimensions to a scatter plot, just like below
- Or being able to have dynamic plot size such as :
- Or again, being able to have a filter in the Interactive Chart (just like the one in the insight tool) would be a great feature to have
- Having Bart chart in differents orientation
Thanks for reading!
When i have a lot of columns in a database and I don't know the specific name of a column, it gets difficult to find it inside the select and summarize tool, for example. If you include a browser inside the most used tools, it would be easier to identify the columns and reduce time. Thanks for your time.
‘Data Scientist’ is a juicy job title to have right now! Not only is it the highest paid job in the industry, but the domain also boasts of high job satisfaction. A report published by Glassdoor shares that ‘Data Scientist’ is the #1 job title offering an employee satisfaction score of 4.2 out of 5. Further, the U.S. Bureau of Labor Statistics reveals that the data science domain will create more than 11.5 million job opportunities by 2026. In other words, people with the knack to set up systems that can generate insights from the burgeoning data are (and will continue to be) an object of desire across industries.
What does it take to become an effective data science whiz, you ask? Take a peek into this article to learn about some habits of successful data scientists.
1. They Continue to Sharpen the Core Skills
Top-notch data scientists not only know their languages, tools, and concepts like the back of their hand but also strive to polish their skillset from time to time. They are experts in the computational (managing big data and real-time data, cloud computing, and unstructured data,) and the statistical (models like regression, clustering, optimization, decision trees, and random forests) aspects of programming. Besides, they possess a strong foundation in data visualization, exploratory data analysis, and machine learning algorithms.
In the world of data science, there’s no such thing as the best language or tool. As a data scientist, you are required to know the latest programming languages and use the tools that are most relevant for the developer and the issues at hand.
For instance, a data scientist cannot choose between Python and R. Both are amongst the most popular programming languages used in this domain, each having their own set of strengths and applications. While Python is a general-purpose language that’s easy to understand, R was developed with the statisticians in mind. Thus, each of these languages has field-specific applications. Here’s an interesting infographic that offers a comparison between Python and R.
Stay updated and learn the trending topics, namely R, SAS, Python, Big Data on Hadoop by enrolling for courses that offer a postgraduate diploma or certification in data science. Such courses can also help you understand and implement concepts like data exploration, regression models, hypothesis testing, Hadoop, and Spark.
2. They Are Always Curious
Influential data scientists have more than just certifications and degrees. They are recognized by their insatiable hunger for product knowledge and the changing industry trends. Their intellectual curiosity encourages them to challenge data types and come up with new ways to discover and interpret data.
Successful data scientists are problem solvers with strong business acumen. They are constantly looking at the problem at hand from varying perspectives and use the most suitable data science tools to offer innovative solutions. They strive to understand the business, customer pain points, changing customer preferences, product cycles, and key industry trends.
Image Source: https://www.forbes.com/sites/bernardmarr/#7dbc05f830c8
Bernard Marr, an influential data scientist, is recognized by several businesses and government organizations for his valuable business insights. Owing to his strong business acumen, he is the most sought-after keynote speaker at the World Economic Forum and business symposia.
3. They Emphasize on Building Strong Networks
Established data scientists constantly strive to strengthen and expand their professional network. Connecting with other professionals in the community not only enables them to grow their influence and authority but also offers a platform for knowledge sharing.
Social sites like Twitter and LinkedIn enable data scientists to network with key influencers and aggregators in the industry. KDNuggets too aims at helping data scientists remain in sync with the latest trends in the world of data science and connect with the global data scientist community.
Further, conferences, namely the Gartner Data and Analytics Summit, Data Platform Summit, and PyData among others offer a platform for data scientists across the globe to discuss and share new ideas and challenges in this domain. The networking opportunities offered at these summits are worth your time and resources.
4. They Are Experts at Data Wrangling and Visualization
Data is everywhere! However, it’s what businesses do with it that determines their success. No wonder, data scientists are expected to be a pro in data munging, visualization, and reporting and brand storytelling. In fact, according to Jefferson Frank, the world’s leading AWS recruiters, data visualization and data mining are one of the top big data skills in 2019.
Further, since data scientists are at the intersection of business, data, and technology they should be experts in data storytelling which the ability to blend hard data with effective and narrative communication.
Learn to fall in love with segregated and non-segregated data. There are several online courses that can train you to work with big data software like Hadoop, MapReduce, and Spark.
5. They Gain Experience through Real-World Projects
Influential data scientists know that the experience of working on real projects is far more precious than merely having a data science qualification on the resume. Consequently, they spend a considerable amount of time researching the internet for projects that can sharpen their data science or machine learning capabilities.
If you desire to propel your career in this domain, focus on gaining hands-on experience through real-world projects. Get on to Google now and find a dataset to work on. Websites like Quandl and GitHub offer open datasets to data scientists and developers, enabling them to host and review codes and manage and collaborate on data science projects.
6. They Know When to Say No
Data scientists have a lot on their plate. They not only extract meaning from the gigantic volumes of data but also use their expertise to solve complex issues in a variety of data niches. Hence, their job description can involve virtually any quantitative work. This means you may have to take up tasks that aren’t relevant to your current project, adversely affecting your productivity.
Smart data scientists understand that the job description of a data scientist is vague. Further, not all organizations have a dedicated data science team on board. Hence, in order to stay productive and develop their niche, successful data scientists have mastered the art of saying no to tasks that are not relevant to their projects.
Without a doubt, data science is one of the most sought-after careers of the century. However, not all data scientists are able to make their mark in this domain. The most exceptional data scientists are those who have incorporated some important habits that have enabled them to taste success.
Include the aforementioned habits in your routine to put yourself on the same path trodden by the most influential data scientists across the globe.
After I scanned the QR code of a contact in Inspire 2019 and 'favorite'd the contact, they are on social wall but it'd be nice to have an iconnection request intiated automatically on other professional networks such as linkedin based on the contact list on social wall. Thanks
Idea: How about delivering the promote package with a model zoo?
Rationale: It takes time to ROI, when you first license any analytics software.
Alteryx Designer comes with nice sample workflows you can start building on top of them and have a
right off the bat.
What if Alteryx Promote comes with;
Some simple and some "deep" models as is;
If we have the flexibility to add new formula any where in the formula tool not just at the bottom of the existing formula's that would be great.
Right now after development if there is change and an additional formula is required which will be input for existing formulas in Formula tool then effort to change is high, if we have the flexibility to add formula at any place in formula tool then the effort will be less.
Alternate Solution : Add one more formula tool before the existing formula tool and include the new formula but i
think if the formula tool has the flexibility to add formula anyplace possible that would be great.
The Inputs to an Alteryx App look very clunky and are not formatted well on the Gallery. The App Inputs look fairly decent when executed from Designer, but the Gallery input tabs looks like a work-in-progress that never got out of beta. We desperately need some way to make the app input tabs aesthetically pleasing and functional. An "artisan" does not like to create a masterpiece app that has a Model-T front end.
When you have a huge workflow with a lot of connections, it turns confusing to identify each one of them. So, i think it would be helpful to include an option that allows users to keep the predetermined connections color or assign, for example, different shades of blue for each one of the outgoing connections of tool.
Currently, you can right click on an input file and convert into a Macro input. however, in order for a fellow user to see what file was used as input, one has to click on it output anchor, copy the data and paste it on a new canvas. It would be nice to right click on the input macro tool and be able to bring up the original input or convert it into a regular input in one step. Thanks