Mr. Data Freak

✨Warm Greetings ✨
“Contact data ages like fish, not wine … it gets worse as it gets older, not better.” — Gregg Thaler

Data Freak Community of ITM
brings to you

📈 Ab Data Bolega 📈
Season 6, Episode 7

Each one is heartily invited in participating wholly in one of the most knowledgeable and trending matter in this Digital era.

Speaker - Mr. Rachit Arora
Co-Founder and Product head of Freight Mango

LinkedIn-
https://www.linkedin.com/in/rachitarora

📈 Topic- Use cases of analytics in logistics 📉
🗓️Date:- 3 December 2022
⏲️Time:- 6:30 pm onwards
📍 Platform- Google Meet
http://meet.google.com/knh-onok-bim



Regards,
Data Freak Community

✨Warm Greetings ✨
“Contact data ages like fish, not wine … it gets worse as it gets older, not better.” — Gregg Thaler

Data Freak Community of ITM
brings to you

📈 Ab Data Bolega 📈
Season 6, Episode 7

Each one is heartily invited in participating wholly in one of the most knowledgeable and trending matter in this Digital era.

Speaker - Mr. Rachit Arora
Co-Founder and Product head of Freight Mango

LinkedIn-
https://www.linkedin.com/in/rachitarora

📈 Topic- Use cases of analytics in logistics 📉
🗓️Date:- 3 December 2022
⏲️Time:- 6:30 pm onwards
📍 Platform- Google meet
(Further details will be shared soon)

Instagram- https://instagram.com/datafreak_community?igshid=YmMyMTA2M2Y=


Regards,
Data Freak Community

As a Founder of Data Freak and Alumni of ITM Buisness school, I got an opportunity to share my experience and chat with new set of management student..
Goodluck, keep believing in yourself good things take time ❤️

Guest session by Ms. Rastogi - Head of Data and Insights at Foodpanda, Thailand.

See you all tomorrow, 5th March 2022 at 2:00 pm IST on Google meet. Link will be mentioned in the bio.

“Data is the new science. Big Data holds the answers.”

ITM Kharghar's Data Freak Community's initiative Ab Data Bolega present’s Guest Session by *Prajwal Yerawar*

He is a Lead Data scientist at Apptware with overall 8 years of pure experience in data science and machine learning.

To know more about these topics, join in for the session

Date : Sunday, January 23rd 2022
Time : 5pm
Platform : Google meet

Session Link: https://meet.google.com/ogf-naaa-pyg

See You All :)

Natural Language Processing (NLP) refers to the AI method of communicating with an intelligent system using a natural language such as English.

Processing of Natural Language is required when you want an intelligent system like a robot to perform as per your instructions, when you want to hear the decision from a dialogue based clinical expert system, etc.

There are generally five steps −

· Lexical Analysis − It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of text into paragraphs, sentences, and words.

· Syntactic Analysis (Parsing) − It involves an analysis of words in the sentence for grammar and arranging words in a manner that shows the relationship among the words. A sentence such as “The school goes to the boy” is rejected by an English syntactic analyzer.

· Semantic Analysis − It draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. It is done by mapping syntactic structures and objects in the task domain. The semantic analyzer disregards sentences such as “hot ice-cream”.

· Discourse Integration − The meaning of any sentence depends upon the meaning of the sentence just before it. Also, it brings about the meaning of an immediately succeeding sentence.

· Pragmatic Analysis − During this, what was said is re-interpreted on what it actually meant. It involves deriving those aspects of language which require real-world knowledge.

Post by Paridhee Toshniwal

"Data really powers everything that we do."

Our esteemed guest, Usha Rengaraju is a Principal Data Scientist and also a 2 x Kaggle Grandmaster. She conducts corporate and faculty training programs.
She studied Data analysis for Life Sciences at Harvard University.
She has been ranked among the Top 10 Data Scientist for 2020 by Analytical India Magazine.

AB DATA BOLEGA
Season 3 Episode 3
Saturday 7 pm

"Data are just summaries of thousands of stories- tell a few of those stories to help make the data meaningful" - Chip & Dan Heath.

ITM Kharghar's, Data Freak Community's initiative Ab Data Bolegaa presents Guest Lecture by Krishna Sandip Karnedy.
Mr. Krishna has 10+ years of experience using agile and hybrid agile. He presently works as an agile coach/scrum master in Deloitte, Ireland.
Topics being covered by Mr.Krishna will be the following topics,
1)Data-driven decision making is key for Agile & Digital Transformation.
2) Agile Developers
3) How Business Analysis is complemented by Data Analytics.
4) Reality of roles in companies - Specialists & Generalists. How do you bring T shaping in Skills?
5) Why domain knowledge is key?

Airmeet link:
https://www.airmeet.com/e/40ed6150-50ba-11eb-b2fa-e1430d77b563

"The goal is to turn data into information, and information into insight" - Carly Fiorina

AB DATA BOLEGA
Season 3 Episode 1

Organized by Data Freak
Speaker - Shiladitya Swarnakar, Analyst-BI at IDFC bank

Mr. Shiladitya has done PGP in Datascience and is also holds a degree of MS in Physics.

During the talk, Mr.Shiladitya shared his views on how today's world runs on data.

He threw light on the basics of data .i.e. there is a lot of data, but there are very few insights about it.
Further, the speaker discussed how presentation and communication skills are important in today's world. Also, the guest speaker explained how it's important to upgrade skills and learn about different software and languages.

At the end of the session, the speaker answered the queries of the students in a very detailed manner.

Datafreak presents
"Ab Data Bolega" Season-3, Episode-1 live with Shiladitya Swarnakar on January 10th at 11 am on Airmeet

Airmeet- https://lnkd.in/e48ENr4

Step 1
As a first step, a thorough definition of the business problem to be addressed is needed. The objective of applying analytics needs to be unambiguously defined. Some examples are customer segmentation of a mortgage portfolio, retention modeling for a postpaid Telco subscription, or fraud detection for credit cards. Defining the perimeter of the analytical modeling exercise requires close collaboration between the data scientists and business experts. Both parties need to agree on a set of key concepts; these may include how we define a customer, transaction, churn, or fraud. Whereas this may seem self-evident, it appears to be a crucial success factor to make sure a common understanding of the goal and some key concepts are agreed on by all involved stakeholders.
Step 2
Next, all source data that could be of potential interest need to be identified. The golden rule here is the more data, the better! The analytical model itself will later decide which data are relevant and which are not for the task at hand. All data will then be gathered and consolidated in a staging area which could be, for example, a data warehouse, data mart, or even a simple spreadsheet file. Some basic exploratory data analysis can then be considered using, for instance, OLAP facilities for multidimensional analysis (e.g., roll-up, drill-down, slicing, and dicing).
Step 3
After we move to the analytics step, an analytical model will be estimated on the preprocessed and transformed data. Depending on the business objective and the exact task at hand, a particular analytical technique will be selected and implemented by the data scientist.
Step 4
Finally, once the results are obtained, they will be interpreted and evaluated by the business experts. Results may be clusters, rules, patterns, or relations, among others, all of which will be called analytical models resulting from applying analytics. Trivial patterns (e.g., an association rule is found stating that spaghetti and spaghetti sauce are often purchased together) that may be detected by the analytical model is interesting as they help to validate the model. But of course, the key issue is to find the unknown yet interesting and actionable patterns (sometimes also referred to as knowledge diamonds) that can provide new insights into your data that can then be translated into new profit opportunities!
Step 5
Once the analytical model has been appropriately validated and approved, it can be put into production as an analytics application (e.g., decision support system, scoring engine). Important considerations here are how to represent the model output in a user-friendly way, how to integrate it with other applications (e.g., marketing campaign management tools, risk engines), and how to make sure the analytical model can be appropriately monitored and back-tested on an ongoing basis.