Self Employed

Wordpress Developer

- Expert in healthcare marketing strategies
- Drive patient acquisition through digital channels
- Develop content for medical professionals and patients
- Optimize online presence for healthcare providers
- Ensure compliance with healthcare regulations

Data driven Digital Marketer

A passionate masters student about data and analytics and how it can help businesses to take effective and efficient decisions.

I am also a healthcare digital marketer with a passion for helping healthcare organizations leverage digital marketing to reach more patients, grow their brands, and achieve their business goals. I have a proven track record of success in developing and executing strategic digital marketing campaigns that drive results. For example, I helped three healthcare professionals double their patient visits to their clinics in 90 days, thanks to my expertise in SEO, content creation, social media marketing, email marketing, and paid advertising.

I am currently pursuing a master's degree in business analytics at DePaul University, where I am learning how to apply data-driven insights and solutions to various business problems and opportunities. I am eager to use my knowledge and skills in business analytics to enhance my digital marketing capabilities and deliver more value to my clients and employers.

I am looking for a position where I can use my skills and experience to help a healthcare organization achieve its digital marketing goals. Contact me today to learn more about how I can help you grow your healthcare business.

I spent ~$100k on a STEM degree in the US, & now only have 1 chance left for H1B. Where does this leave me? 

It’s been 2 days since I found out that I wasn’t selected in the H1B lottery even on my second chance. 

This means that if I don’t get H1B in my last chance next year, I have to leave the U.S. 

That’s all I have thought about since the past 2 days, feeling dejected and lost. 

While I was planning to move to the States, my sole focus was on getting into a top school, and I did.

I was naive enough to think that the hard part was over.

Far from it.

Life of an immigrant in the U.S. is in a constant state of worry, anxiety, hard work, & luck. 

Getting into a good school only marks the beginning of the struggles of an international student’s life. 

As soon as we land, we start worrying about landing a good internship. 

Post that, we’re stressed about landing a good job within 90 days of graduation or else have to go back to our home countries. 

Post that, STEM students get 3 chances at the H1B lottery, & only randomness decides whether you get to stay back in the U.S. after 3 years or not. 

By some stroke of luck, if you get an H1B, you’re worried about getting laid off. If you get laid off, you get 60 days to find another job or else you have to leave the country yet again. 

I have been feeling a little bogged down since the past 2 days. 

What does all the struggle amount to at the end of the day, if you don’t get an H1B? One can argue everyone knows about these factors before making the move, and that there are alternatives to stay back. 

True, but the precariousness of it all takes an emotional & mental toll. 

Not just on me, but on every single international student I have spoken to so far. 

A lot of them manage to get an H1B, green card (after decades) and settle down eventually, but no one talks about the ones who have had to go back to their home countries. And what a failure it makes them feel like, despite it not being their fault. 

The reality is eclipsed by the success stories on & off LinkedIn! 

To those of you whose H1B didn’t come through on the second or third attempt this year, I know my thoughts are resonating with you. 

It’s a tough ride, and it never really gets easy. 

So, to everyone who’s filling out US school applications, this post is your reminder of tough times ahead, a preview of a life of uncertainty & struggles, which may or may not lead to a happy ending. 

This post isn’t meant to discourage anyone, but to let you know the reality, which is far from ideal. 

Make your choices wisely! :) 

And if you’re in the same boat as me, hang in there! It’s not looking great now, but it’s not over yet. 

P.S. I’m Shreya Mishra, a former international student turned TPM at Visa. I talk about my experiences as an immigrant, student & corporate woman in the U.S. Follow along to keep learning with me & engage with the post if you found it helpful! 

#H1B #stemopt #internationalstudents #immigration #gradschool

Sourabh Rodagi

When you realize selling DSA courses actually makes you 55 LPA unlike Microsoft with 55LPA CTC & 15 LPA base salary xD.

#humor #joke 

SOLIDWORKS 2022 STEP-BY-STEP GUIDE 
Download: https://shorturl.at/tuyKO

🟢Get your Tools: https://amzn.to/4amUFdS
🟢Subscribe Here: https://cutt.ly/IwGsGjnJ



Video credit to respective owners. DM for Claim

//WHAT DOES A CONSULTANT ACTUALLY DO?!

Consultants. 

They live among us in our cities, our suburbs, and in fancy airport lounges.

But what does a consultant actually do? 

Nobody knows.

But at least they are pretty good at sending you insanely high invoices...

P.S.
The reason we reject the "consultant" label is because we emphasize execution over just providing a presentation and leaving the customer to figure things out on their own 😅🤌


credits @ Dan Toomey 
———

This is how we bring your ideas to market in one week only: 
https://lnkd.in/eq4TvNC4

#innovation #creativity #future #whatinspiresme #innoweek #venturestudio #companybuilder 

All rights and credits are reserved to the respective owner(s). 
Contact me to add you as a reference or content removal.

♻️ Repost to help your network to bring their ideas to market.
And follow Dipl.-Ing. Lars Behrendt for more posts like this.

You are limiting your data career opportunities by focusing on specific job titles

Here's the big problem when you approach your job search by using specific titles

Most organizations have their own in-house definition of titles and job responsibilities

A data analyst might be referred to as a business analyst or a business intelligence analyst depending on the organization

So when you search for "Data Analyst" all the roles that pop-up will be very specific 

You want to change how you search and the focus of your search for a broader scope 

You should change your search by removing any keywords that make it too narrow. For example, instead of "Data Analyst", try searching "Analyst" to include all possible variations of Analyst roles

You should change your focus from a title-based search to a skill-based search. For example, instead of looking for a specific job title, do a boolean search to include key skills for the job. A boolean search for an Analyst role might look like this: "Analyst" AND ("Excel" OR "SQL" OR "Tableau" OR "PowerBI")

Recruiters follow a similar approach when scouting for talent

Your job search strategies are as important as the investments in acquiring the skills 

Broaden your search for maximum impact

---
P.S: I am sharing a list of data analytics roles you might not have considered. Start including them in your search.

19 Data Analytics Roles to Consider in Your Job Search

For all students affected by the US H-1B visa results, returning to India might not be such a bad thing!

Here's my story:

After my MIT PhD finished in 2022, I was interviewing with a number of companies such as Amazon for the position of a machine learning engineer. These roles (such as Facebook, Google and Microsoft) were offering an average annual salary of 200,000 US dollars per year (~ 1.5 crore per year). It was the perfect start to the so called "American Dream".

However, I declined these jobs and came back to India. I decided to build something of my own and contribute to the growth story of India. So, how is life panning out for me? What are my learnings so far? Let me outline some key points:

(a) Satisfaction: Since coming back to India, I have visited a large number of schools in different parts of the country. It is pretty common for me to start my day at 7am and end up visiting 8-10 schools by 7pm at night. I am tired and exhausted. But the satisfaction of trying every day to improve the education system in India is the best feeling in the world. 

If I were in the USA: I would have been working for a corporate giant from 8am - 6pm. At the end of day, maybe I would have contributed in making the Messenger app of Facebook 5% more efficient or the Amazon Pay experience smoother for its North American customers or made the rocket propulsion software tool cooler for SpaceX. But I am pretty sure, I would not have the satisfaction which I feel every single day in India, chasing my dream. One more foot soldier in the army of Mark Zuckerberg, Elon Musk? No, not for me. 

(b) Impact: I came to India with the vision of revolutionizing the Indian education system. Still a long way to do, but we take small steps to reach our target every single day. I am the co-founder of 2 EdTech startups. So far, our products have reached 100+ schools, 1000+ teachers and 5000+ students. 

If I were in the USA: I wouldn’t have been able to chase my dream and reach my vision of changing the Indian education system.

(c) Uncertainty: As I sit in a cafe right now, I am pretty unsure what my next week would look like. Maybe I would have to visit Chandigarh for an education conference. Maybe I will have to stay in Mumbai for the full week to visit major schools in the Andheri region. Maybe I will visit Jalandhar to give a keynote talk to which I have been invited to. Maybe, me and my co-founders will have a new office. So many exciting things “can” happen.

If I were in the USA: Well, next week would be pretty similar to this week. Maybe some new challenges in work. But largely, life would have been predictable. Predictability is good, I don’t deny it. But the thrill of the unknown motivates me to get out of bed and explore new possibilities every single day.

Not everything is easy. Not having a stable job income is definitely challenging. However, I am learning to deal with these challenges. 

Overall, I don't regret coming back for a single day!

Major Difference Between POWER BI, TABLEAU And LOOKER:

R-squared is one of the most commonly used metrics to measure performance. But it took me 2 years to figure out mistakes that were killing my regression models. In 2 minutes, I'll share how I fixed 2 years of mistakes (and made 50% more accurate models than my peers). Let's go: 

1. R-squared (R2): Is a statistical measure used in regression models that provides a measure of how well the observed outcomes are replicated by the model, based on the proportion of total variation of outcomes explained by the model. 

2. Range (0 to 1): R2 ranges from 0 to 1. A higher R2 value indicates a better fit between the prediction and the actual data. For example, an R2 value of 0.70 suggests that 70% of the variance in the dependent variable is predictable from the independent variable(s).

3. Mistakes I made that took me 2 years to fix: I made 2 mistakes early on when I was modeling and in particular making forecasts (where the model developed would predict the next n-steps in a sequence). Mistake 1: Using in-sample metrics. Mistake 2: Using Variance explained only. Here's how I fixed them.

4. Mistake 1 (Using In-Sample Metrics): In stats, we're taught to develop a model with a high R-squared or high adjusted R-squared. So what did I do? I tried to increase R-squared of the model, which is produced from the training data. This led to overfitting. My forecast models did not generalize well, and were erratic. The solution: I switched to Cross Validation. This helped, but something was still missing. 

5. Mistake 2 (Maximizing Variance Explained isn't Always the Right Solution): R-squared is a measure of variance explained. Meaning that the more the prediction varies with the actual data, the higher the R-squared. This is not necessarily desirable in Forecasting. Rather, in forecasting I want a model that is stable around the mean of the unseen data. To get this, I need a different metric. 

6. A better metric (RMSE): Combining R-squared with Root Mean Squared Error (RMSE) was the solution. I would measure both R2 and RMSE for my forecast models, and this gave me a better picture of the model performance. If the model did well on RMSE, I knew the model had high stability. If the model did well on R-squared, I knew the model did well predicting the peaks and valleys (variance explained).

And that's how I began making forecast models that were 50% more accurate than my peers. 

===

Need help applying data science to business?

I'd like to help. Here's how:

👉 My Free 5-Day Course (How to Solve Business Problems with Data Science): https://lnkd.in/e_EkiuFD

👉 Learn ChatGPT for Data Science: https://lnkd.in/gCvh6UAy

If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

Data Visualization Overview

Get Ultimate PDF : https://lnkd.in/dkUhFcQR


Image Credit: Ravit Jain

#DataVisualization #DataVisualizationtool #database #databasemanagement #datalake #datawarehouse #datasecurity #databasesecurity #vectordatabase #clouddatabase

It took me 6 weeks to learn overfitting. I'll share in 6 minutes (business case study included). Let's dive in:

1. Overfitting is a common issue in machine learning and statistical modeling. It occurs when a model is too complex and captures not only the underlying pattern in the data but also the noise.

2. Key Characteristics of Overfitting: High Performance on Training Data, Poor Performance on Test Data, Overly Complex with many parameters, Sensitive to minor fluctuations in training data (not robust).

3. How to Avoid Overfitting (and Underfitting): The goal is to get a model trained to the point where it's robust (not overly sensitive) and generalizes well to new data (unseen during model training). How we do this is to balance bias and variance tradeoff. Common techniques: K-Fold Cross Validation, Regularization (penalizing features), and even simplifying the model. 

4. How I learned about overfitting (business case): I was making a forecast model using linear regression. The model had dozens of features: lags, external regressors, economic features, calendar features... You name it, I included it. And the model did well (on the training data). The problem came when I put my first forecast model into production...

5. Lack of Stability (is a nice way to put it): My model went out-of-wack. The linear regression predicted demand for certain products 100X more than it's recent trends. And luckily the demand planner called me out on it before the purchase orders went into effect. 

6. I learned a lot from this: Linear regression models can be highly sensitive. I switched to penalized regression (elastic net) and the model became much more stable. Luckily my organization knew I was onto something, and I was given more chances to improve. 

7. The end result: We actually called the end of the Oil Recession of 2016 with my model, and workforce planning was ready to meet the increased demand. This saved us 3 months of inventory time and put us in a competitive advantage when orders began ramping up. 

Estimated savings: 10% of sales x 3 months = $6,000,000. 

Pretty shocking what a couple data science skills can do for a business. 

===

Need help applying data science to business?

I'd like to help. Here's how:

👉 My Free 10 Skills Webinar: I put together a free on-demand workshop that covers the 10 skills that helped me make the transition to Data Scientist: https://lnkd.in/gbEBVf5f

If you like this post, please reshare ♻️ it so others can get value (follow me, 🔥 Matt Dancho 🔥 for more data science concepts).

Let's talk about learning. You know, a lot of us soak up knowledge without really putting it into practice. But here's the thing: practice is key to truly mastering what you've learned. It's like deepening your understanding and making it stick.

But before that, if you are new to data, I highly recommend you read this article before learning any data tools: https://lnkd.in/dpDDYGjw

So, I've got something exciting for you! Comprehensive datasets to dive into or refine your analysis skills. These resources are pure gold:

Datasets ready for exploration:

• https://data.gov/
• https://data.world/
• https://data.gov.in/
• https://www.data.gov.uk/
• https://www.kaggle.com/
• https://stats.govt.nz/
• https://mockaroo.com/
• https://lnkd.in/dJaHm-z2
• https://lnkd.in/dNAHXdve
• Google Data Search: https://lnkd.in/dx4uHJcY

Before plunging into any dataset, please take a moment to understand its context. Knowing where the data comes from and why it exists can give you some seriously valuable insights and guide your analysis in a meaningful direction.

If you found this helpful, feel free to repost it and share the wealth!

#DataDiscovery #DataResources #DataAnalysis #DataJourney #DataSets #DataExploration #DataMining #BigData #DataTools #DataScience #Analytics #DataDriven #DataEnthusiasts #DataChallenges

Best way to stand out early in your data career?

Think like a business owner 💡

👉 Talk to stakeholders to understand their motivations
👉 Build domain knowledge to learn the nuances of the business
👉 Clearly articulate how your analysis ties to specific goals or KPIs
👉 Draft a measurement plan before you even touch the data

Early in my career all I wanted to do was build fancy reports and dashboards, but as soon as I started thinking this way everything changed.

Not only did I start earning respect and recognition from management, but I began to actually see (and measure) the impact of my work. This was probably the single biggest catalyst in my career growth and development as an analyst.

So to all the seasoned pros out there, what other advice would you give to help an analyst accelerate their career?

A-Z #Statistics topics for Data Science:

➡ Beginner:

- Mean
- Median
- Mode
- Variance
- Standard deviation
- Normal distribution 
- Binomial distribution
- Central Limit Theorem
- Hypothesis testing
- Confidence intervals
- Regression analysis
- Correlation coefficient
- Linear regression
- Residual analysis
- Analysis of Variance
- Time series analysis
- Cross-validation
- Bias-variance tradeoff
- Overfitting
- Underfitting
- Regularization 
- Decision trees
- Ensemble methods
- Naive Bayes classifier
- K-nearest neighbors
- Support Vector Machines 

➡ Intermediate:

- Multiple regression
- Logistic regression
- Autocorrelation
- Feature scaling
- Feature engineering
- Imputation
- Outlier detection
- Sampling techniques
- A/B testing
- Nonparametric statistics
- Bayesian statistics
- Maximum likelihood estimation 
- Expectation-Maximization algorithm
- Markov models

➡ Advanced:

- Principal Component Analysis 
- Singular Value Decomposition 
- K-means clustering
- hierarchical clustering
- Dimensionality reduction
- Monte Carlo methods
- Gibbs sampling
- Dirichlet distribution
- Gaussian mixture models (GMM)
- Hidden Markov models (HMM)
- Advanced time series analysis

PS: Did I miss anything?

𝗕𝗼𝗻𝘂𝘀: I've made a practical Machine Learning Guide for beginners, download from here:
(https://lnkd.in/gZKqbQmn)

📍 Follow ----> Leon Jose 👋

Set the 🔔 notification on my page, don’t miss a post!

#datascience #statistics 

Less than 1% of data scientists can do this skill for business analysis. 

But, it's a treasure trove for companies.

Let me explain. 🧵

Whether you realize it or not, your business runs off of customers. 

And how they work is based on principles of social psychology.

If you understand which customers are similar, then you know how to market to them… 

And knowing their triggers is like adding fuel to a fire. 🔥

But here's the problem... 90% of data scientists have NO CLUE how to do it.

Why data scientists are struggling with Customer Segmentation:

As databases increase in size, traditional methods like K-means or HDBSCAN can fail or become slow.

But there's a solution. And I want to share it LIVE on Wednesday.

What's inside this Learning Lab? You will discover:

✅ Discover how to make large-scale clustering models with 1MM+ customers (that saves the company $1,000,000 per year)! *** THIS IS A GAME CHANGER ***

✅ Gain a skill that can earn you $150,000 or more (and become the customer segmentation expert for your company).

✅ Uncover the insider tricks to fill your data science portfolio with data science projects companies need RIGHT NOW. (Instantly become in-demand)

What's your next step?

Just register & attend live.

Seats are limited.

👉Register Here: https://lnkd.in/ePrPhbB2

Hello, Connections!
𝐂𝐨𝐥𝐥𝐞𝐜𝐭, 𝐖𝐫𝐚𝐧𝐠𝐥𝐞 & 𝐂𝐥𝐞𝐚𝐧 𝐈𝐍 𝐄𝐗𝐂𝐄𝐋 - 𝐒𝐐𝐋- 𝐏𝐘𝐓𝐇𝐎𝐍
Collecting, wrangling, and cleaning data are essential steps in the data science process that involve gathering raw data, preparing it for analysis, and ensuring its quality and integrity. 

The below PDF is handy for Freshers. 😎

pdf credit - Andrew C. Madson 

⏩ 𝐉𝐨𝐢𝐧 𝐦𝐲 𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦 𝐂𝐡𝐚𝐧𝐧𝐞𝐥 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 & 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬:
https://t.me/LK_Data_world
⏩ 𝐅𝐨𝐥𝐥𝐨𝐰 𝐨𝐧 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 - https://lnkd.in/g2FqfiPk

⏩ If you found this PDF informative, save and repost it🔁.
⏩ Lovee Kumar 🛎 

#data #dataanalytics #dataanalyst #dataengineer #datascience #skillset
#selflearning #python #excel #sql #cleaning #linkedin #connections

Collect,Wrangle,Clean in excel-sql-python

Last night, my amazing team in the Kellstadt Career Management Center within the DePaul Driehaus College of Business hosted our inaugural Kellstadt Networking Night! These events serve as the opportunity to bring together our talented KGSB students with our community of alumni and employer partners in an effort to network and create connections between our College and Industry. Beyond thankful for our alumni and industry partners within the Business Analytics space for taking the time out of their day to engage with our current students!

Moving forward, the Kellstadt Career Management Center will be hosting six Kellstadt Networking Nights each academic year across a variety of industries and functions. If you are a DePaul University alum and/or industry professional interested in engaging with our graduate business students, please contact me directly and I'm happy to share information about future opportunities to get involved! Feel free to email me directly at hpacko@depaul.edu 

Here's to another successful Connect with Kellstadt mixer! Thank you to new and current students, staff (especially the Career Management Center), and faculty for coming. 

We hope you all enjoyed great conversation, networking, and sweet treats from Chartwell. We look forward to seeing you in the Fall! 

As always, it is a delight to organize these events with the KGSB Academic Advising Team, Rachel Hamil, M.Ed. and Ellen Dawson Van. (We will greatly miss Rachel's teamwork for next year's events). 

Thank you to Assistant Dean Cathy Alonzi Gill and Associate Dean Jaclyn Jensen for your support. 

We are always grateful for assistance of Twyla Covey and Beatriz C. throughout the year. We couldn't do it without you! 

And big thanks to Kathy Kelly Hillegonds for lending me her camera!  

DePaul Driehaus College of Business

29 Most Popular Book Summary Compilation you will ever find on LinkedIn

Learn from the most brilliant minds in the world, and unlock your full potential.

However, I recommend buying the book and reading a full book that gives the most value to your time

#booksummary 

Most Popular Book Summary Compilation 

📊Update: Day 41 of the 100-day Data Analyst challenge.

🎯Objective: " 🎬📊 Exciting Insights from TMDB 5000 Movie Dataset Analysis Project! 🎥💡"

🚀 The objective of this project is to understand what variables define the success of films🎬, a successful movie being a movie with higher revenue and score. Let's unravel some intriguing findings from my analysis:

🔍 Data Cleaning: (Source: Kaggle) the first task was to browse all the data and replace the json character in string format using the json.loads() method.
Dealt with the null values, duplicates, zero values, data type of columns. 

 📊 Exploratory Data Analysis (EDA) :

💡 Insights:

-Profit and Popularity shows a positive but low correlation.
-Wednesday emerges as the day with the highest average profit and popularity, while Friday lags behind.
-Majority of movies the revenues increases when the rating increase
-The rating of movies increases especially when the number of actors is around 6 and 7 main actors and in most of the cases the revenue increases when the number of actors increases.
-Medium films steal the spotlight, consistently outperforming other categories in terms of average profits.
-Short films exhibit increasing profit trends, with 2015 and 2016 surpassing the overall profit mean.
-Long films garner the highest average vote ratings in 2009.
-Action genre reigns supreme in revenue generation, with iconic movies like Avatar setting the bar high.
-Warner Bros emerges as the top production company, leading the pack in the number of movies released.
-Despite the dominance of male-centric narratives, there's potential for diversification and inclusivity in the industry.

🤖 Model Training and Evaluation: 

 ⚙️ Data Preprocessing Steps:

1.Restriction of the actors and production companies features:
2.Creating dummy variables:
3.Standardization:

🤖 Model Selection:

Leveraging different regression techniques such as Ordinary Least Squares (OLS) regression, Ridge regression and XGboost, try to find the most suitable by evaluating their performance. I aim to predict revenue and average ratings. XGboost outperforms both Ridge Regression and OLS.
 
🚀Conclusions: As a movie enthusiast or a key player in the film industry, understanding the dynamics of box office success and movie quality is paramount. 🌟 

🔮 Stay tuned as we unravel more fascinating discoveries 

#DataDrivenInsights #DataScience #MovieAnalytics #DataInsights #FilmIndustry #BoxOfficeSuccess #QualityMovies #PredictiveModeling #DataMining #dataanalytics #dataanalyst #DataAnalysis #Python #DataInsights #100daysofdataanalytics #datajourney #dataskills #datadriveninsights

TMDB 5000

VantEdge Partners is hiring for the 2025 Summer Analyst positions. Contact me directly if you're interested in learning more!

Taplio

Sourabh Rodagi's Linkedin Analytics

Sourabh Rodagi

Sourabh Rodagi's Best Posts (last 30 days)

Want to drive more opportunities from LinkedIn?

Taplio

.css-1jgreyo{background-image:linear-gradient(284deg, rgba(39,43,72,1) 0%, rgba(33,47,183,1) 100%);color:transparent;-webkit-background-clip:text;background-clip:text;}Sourabh Rodagi's Linkedin Analytics

Sourabh Rodagi

Sourabh Rodagi's Best Posts .css-qqfgvy{font-size:var(--chakra-fontSizes-md);}(last 30 days)

Want to drive more opportunities from LinkedIn?

Sourabh Rodagi's Linkedin Analytics

Sourabh Rodagi's Best Posts (last 30 days)