When integrating the work of non-data scientists into data projects in an expressive way, you need more fundamental modification in various fields like data tooling and added powerful features that let the data analysts to bring impactful work without coding.

Dataiku eventually provides a powerful feature in many visual analysis layers, data analysts, prepare, enrich, and envision several types of structured and unstructured data. Whereas, the data scientists focus on Dataiku as well as coding or so forth tools they like the effective collaboration on data projects is unified.

What could be the code-free Dataiku part in the data analyzing & IT field? Did they have high demand? And related- relax we have drafted the best answer for all your questions here. Let’s have an in-depth 5 effective ways you can achieve in Dataiku without the support of coding.

• Smart Data IngestionAct according to Dates and Times
• Clearing out complex text fields
• Streaming Twitter Data & Creating machine learning models
• Combining / Joining datasets & Handling Geographical Data

Smart Data Ingestion

Be in touch with IT, request data access, wait days (or weeks), get access, rinse, repeat. Have you heard about it? Dataiku has an improved way of uploading datasets to work with XLSX or CSV files. Or else, aids in connecting to other sources like databases, server-hosted files, configurations to business applications, and more related findings.

Eventually, Dataiku is much more peculiar- supports you instantly to upload a CSV or Excel file, and it repeatedly identifies the separators and character encoding. Additionally, display a preview of the dataset to inspect whether all are sorted, regarding the number of lines, and let you modify some parameters (like skipping lines, handling column headers, etc.) to ensure that the data is reliable right after the process of uploading.

Other useful code-free smart data ingestion features to improve accessibility are:

• Helps in combing the data of all sizes, shapes, and locations without million-row restriction
• Repeatedly stack matching files into one dataset using the drag & drop technique into an interface
• Supports merging spreadsheet tabs into a single dataset
• Enabled with renaming your columns and setting data types directly in the schema panel. In which certain columns are constantly maintained in specific formats.

Act according to Dates and Times

Here, we have more time-based features which are usual in data-driven practice cases and challenging to work with. In reference to the original format, you are supposed to do some heavy recoding to parse the dates into a standard date feature.

Using this Dataiku, you can implement a smart date processor that helps to recognize probable date formats and suggest diverse parsing options. Preview how well each option is getting utilized.

Dataiku also permits you to improve data and create time-based features, that consist of

• Haul out date components (In specific with -month, hour, day of the week, week of the year, etc.)
• Calculating differences between date columns
• Flagging national holidays (Pin your holidays at ease)

Clearing out complex text fields

Do you have trouble with messy text fields & managing? Sorting the data might frustrate you– To avoid such complex issues in cleaning and structuring data, Dataiku helps you to perform code-free text cleaning processors including split, find a& replace, truncate, and more.

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Streaming Twitter Data & Creating machine learning models

Dataiku is effectively enabled with a simple connector which can be easily retrieved from Tweets or related details like user handle, location, hashtags, etc. It supports you with keywords or hashtags. Once the data has been captured, visual text analysis features can cluster the same kind of Tweets. Eventually, split them into words or n-grams, remove stop words, and more. All of the above are the proficient initial move towards performing sentiment analysis without the help of coding.

On the other hand, Dataiku permits non-coders to learn algorithms, work with predictions, recognize clusters, and extract beneficial information regarding the features without codes.

Combining / Joining datasets & Handling Geographical Data

Combing datasets can enhance the data — basically, retrieving columns from one dataset or using the tab into a reference dataset (VLOOKUP). Dataiku is the main element to perform any analysis, which can be that quick to bring several tasks in no time (Eventually both in computation time and joining standards).

In Dataiku, amalgamation data sources are shortened (requires all the visual, no VLOOKUP) via precise processors that can effortlessly retrieve all data from other datasets or supports to join the same specific, fine-tuned keys and criteria.

Alternatively, the Geospatial analysis is dangerous in a place with more number of use cases. Dataiku features also support working with locations, most probably with the followings:

• Supports retrieving a latitude and longitude from any address under OpenStreetMap or Bing Maps API
• Enhancing the latitude and longitude using administrative information (like city, state, department, etc.)
• The Dataiku chart engine is also available to draw scatter maps and heat maps for various levels of aggregation.

Conclusion:

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