Siddharth Gupta Excel | Python | R | Stata | SPSS | Eviews
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I started teaching my friends for school exams when I was 15 years old. From there, I learned how to explain complex ideas in a simple way. In just one class, I can understand the best way to teach a student and change my teaching style to help them learn better. I make sure to explain concepts with many examples and give plenty of practice questions so students can understand deeply. I explain a concept until the student understands it well, and always connect the ideas to real-world situations, making learning engaging and practical.

My Data Science Skills

1) Data Cleaning: I find a clean dataset as the stepping stone to insightful analysis. Over numerous projects, I have honed my skills in identifying and correcting inconsistencies that could derail the analysis.
2) Project Management using CRISP DM: In managing various projects, I have always relied on the CRISP DM framework. It has been my roadmap in navigating from understanding business objectives to delivering actionable insights.
3) Data Modelling: Building models to find out hidden patterns or to predict outcomes has been one of the most rewarding aspects of my work. Each project presents a unique challenge that sharpens my modelling skills further.
4) Data Transformation: I have often transformed raw, unstructured data into a structured format that's ripe for analysis. It's like preparing the soil before sowing the seeds.
5) Outlier Detection: I have developed a keen eye for spotting outliers that could potentially skew the analysis. Addressing these outliers has often been the key to a more accurate and insightful analysis.
6) Statistical Analysis: Crunching the numbers and deriving meaningful insights has always been fascinating. Through various projects, I have applied statistical techniques to draw conclusions that drive decision-making.
7) Hypothesis Testing (Parametric and Non-Parametric): I often find myself testing hypotheses to validate assumptions. It's a rigorous process that has always added value to my analysis.
8) Simple and Multiple Linear Regression: Employing regression analysis to understand the relationships between variables has been a common practice in my projects. It's intriguing to see how variables interact with each other.
9) Correlation: I frequently analyze the correlation between variables to understand their relationships. It's the initial step that gives the way for a deeper analysis.
10) Confidence Interval: Estimating confidence intervals has been significant in my projects. It helps in setting realistic expectations and understanding the range of possibilities.
11) Research Design: Designing the research process meticulously has always been vital. It sets the stage for how data will be collected and analyzed.
12) Qualitative and Quantitative Research: I have engaged in both qualitative and quantitative research to get a holistic view. It’s like listening to both sides of the story.
13) Management Report Writing & Data Presentations: Writing reports to convey my findings has been a regular part of my workflow. I ensure my reports are clear, concise, and actionable.
14) Time Series Forecasting (Stationary and Non-Stationary): I enjoy forecasting as it feels like getting a glimpse into the future. I have a good handle on using ARIMA models, which has been quite useful in my projects, especially when there’s seasonality in the time series data. It’s always interesting to see how past data helps in predicting future trends.
15) Probability Distributions: I often use probability distributions to understand if random variables of real life follow some of the mathematical distributions.
16) Machine Learning Model Development and Hyperparameter Tuning: Developing machine learning models and tuning them for better performance has been a rewarding journey. Each model is a learning experience.
17) Statistical Process Control: Monitoring processes to ensure they perform as expected has been a key part of maintaining quality in my projects.
18) Quality Management: I strive for consistency and quality in processes. It’s about keeping the engine running smoothly.
19) Sampling Methods and Central Limit Theorem: Through sampling, I often find a mirror reflecting the larger population. It’s a technique that has served me well in many projects.
20) Survey Design and Analysis: Designing surveys and analyzing the responses has been instrumental in gathering insights directly from the source.
21) Decision Theory and Science: Applying systematic approaches to decision-making has been a part of making well-informed and justified choices in my projects.
22) Marketing, HR, Supply Chain, Financial Analytics, and Econometrics: I have explored various business domains with data analysis, each domain presents unique challenges and learning experiences.
23) Clustering and Classification: Identifying groups within data and categorizing them has always been like solving a part of a larger puzzle.
24) Feature Engineering and Selection: Selecting the right features for a model is crucial. It’s like choosing the right ingredients for a recipe, each choice significantly impacts the outcome.
25) Linear Algebra: Linear Algebra has been a fascinating tool for me, especially when it comes to uncovering patterns in data. Through the use of eigenvalues and eigenvectors, I've been able to identify patterns in datasets, identifying underlying structures and relationships. It's like having a key to unlock a treasure of insights which otherwise remain hidden. Each application of Linear Algebra in my projects has not only sharpened my understanding but also enriched the depth of analysis, leading to more informed and insightful conclusions.

Tools
The tools in my kit are like trusted companions on every data adventure:

1) R: I find R to be a powerful ally when it comes to statistical analysis and machine learning. Its vast library of packages has often allowed me to tailor my analysis to the unique needs of each project.
2) Tableau/Power BI: Visualizing data in Tableau or Power BI has always been like painting a picture with numbers. It’s amazing how these tools help in telling a compelling story that’s hidden in the data.
3) MS Excel: Excel has been a reliable companion for preliminary data sleuthing. Its simplicity and versatility have often been instrumental in crunching numbers quickly.
4) SPSS: Hypothesis testing and regression analysis are some of the tasks where I have found SPSS to be quite adept. It’s a tool that has served me well in many statistical endeavors.
5) Stata: My econometrics explorations have often been supported by Stata. It’s a robust tool that stands strong when diving into economic data.
6) SQL: Querying databases with SQL feels like having a meaningful conversation with data, asking it to reveal its secrets.
7) Python: The versatility of Python has made it a go-to for various data analysis and machine learning tasks. It’s like having a Swiss army knife in my toolkit.
8) Minitab: Minitab has been quite handy when it comes to statistical analysis and quality control projects. Its user-friendly interface makes complex analysis seem simpler.
9) Matlab: Matlab has been my partner in numerical computing tasks. It’s been particularly useful in algorithm development and data visualization.
10) Statcrunch: Statcrunch is a gem for basic statistical tasks. It’s straightforward and has helped me in quickly analyzing data and generating insights.

Industry Experience

My work experience in different companies has given me a lot of real examples that I use in my teaching. Here are some projects I have worked on:

1) Data Modelling & Dashboarding: Worked with an online clothing brand to find out why customer returns were increasing and made a dashboard to track packaging and dispatch processes.
2) Profit Optimization: Helped a restaurant manage during COVID-19 by reducing menu items and finding which dishes sold the most on different days to keep operations running smoothly.
3) Improvement in Lead Conversion: Helped an asset management company identify the reasons they were not able to convert website leads to customers and increased their conversion rate by 15%.
4) Loan Repaying Capacity Identification: Developed a machine learning system to better predict loan repayment abilities of potential customers for a financial company.
5) AI Automation: Created a machine learning model for a manufacturing company to predict backorders, helping them manage holding and carrying costs better.

These projects show my ability to use data science to solve real problems, which I bring into my teaching to prepare students for real-world challenges in their future careers.

Subjects

  • Power BI Beginner-Expert

  • Data analysis Beginner-Expert

  • Data analysis R programming Beginner-Expert

  • Data analysis and visualization Beginner-Expert

  • Machine learning using R Beginner-Expert

  • Data Analysis in R/Python Beginner-Expert

  • Machine learning Python Beginner-Expert

  • Data Analysis with Excel, Access and sql Beginner-Expert

  • Excel Dashboard Reporting Beginner-Expert

  • Machine Learning with Advanced Mathematics Beginner-Expert

  • Machine learning projects Beginner-Expert

Experience

  • Freelancer (May, 2012–Nov, 2021) at Freelancer and online tutor
    Namaste! I am Siddharth Gupta from India. I have been working as a freelance data scientist from past 5 years. I have worked with numerous companies across the globe to make them understand and build their data pipeline in order to increase operational efficiency and profit margins. I have worked for marketing analytics segment to improve customer targeting and optimizing their advertising spent. Apart from that I love to teach and I have been teaching from past 9 years. I have taught MS Excel, Python, R, Business Analytics, Data Visualization using Tableau, Power BI, Statistics and Operations Management to more than 1000 students across the globe out of which majority was from US Universities.

    My Specialties
    1) Data Processing
    2) Data Pipeline Building
    3) Marketing Analytics
    4) SOP development
    5) Python and R language
    6) Stata
    7) JMP Pro
    8) EViews
    9) MS Excel, MS Access, MS Visio
    10) SQL
    11) Financial Analytics
    12) Dashboard conception and development using Plotly, R Shiny, Tableau, Power BI

Education

  • Google Data Analytics (Jan, 2021–Apr, 2021) from Google Data Analytics Professional Certificate | Coursera
  • MBA Finance (Jun, 2015–Jun, 2017) from SMUDE
  • Bachelor of Engineering (Aug, 2008–Aug, 2012) from SGSITS, Indore

Fee details

    ₹500–3,000/hour (US$5.90–35.37/hour)

    Fee variations depend on the complexity of the task and effort required. I commit only the correct timing so you can be rest assured that whatever time I commit is actual time required to finish the task.

Courses offered

  • Statistics Using Python from Scratch (With 400 practice exercises)

    • ₹10000
    • Duration: 40 days
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi
    • Certificate provided: No
    The most toughest strategic decisions in business depends on inferential statistics and hypothesis testing. I have mastered the art of understanding Hypothesis Testing and other concepts of statistics on Python and use them regularly in decision making for my clients. I can help you get the complete understanding of the same.

  • Statistics Using R from Scratch (With 387 practice exercises)

    • ₹20000
    • Duration: 30 days
    • Delivery mode: Online
    • Group size: 3
    • Instruction language: English, Hindi
    • Certificate provided: No
    R language is meant to be the programming language for Non-programmers. However, it is on its way to become a full fledge programming language backed by strong team of statisticians who made it the most suitable for statistical analysis.
    This course cover Descriptive, Inferential, Predictive, and Prescriptive statistics to left no stone unturned in mastering R language.

  • Build Better Dashboards with Tableau/Power BI

    • ₹25000
    • Duration: 60 days
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi
    • Certificate provided: No
    Dashboards are the most powerful tool for decision makers be it in a Nuclear Power plant monitoring the real time activity of Uranium rods or be it the CEO of MNC looking for an update on a marketing campaign. Dashboards are classified as operational, managerial and strategic depending on their application and this course cover all three forms along with some amazing features for New Age SaaS or VC funded companies.
    Do you know the journey of building dashboards do not start with data but with the identifying Key Performance Indicators of any business and then collecting data? Come join me on this journey and learn the art of story telling with dashboards.

    Irrespective of what tool you want to use, the basic design principles will remain same. We will learn how to do it on Excel and Tableau OR Power BI depending on your needs.

  • Linear Algebra for Machine Learning/ Data Science

    • ₹25000
    • Duration: 45 days
    • Delivery mode: Online
    • Group size: Individual
    • Instruction language: English, Hindi
    • Certificate provided: No
    Confused about what to do with mathematics in Machine Learning or Data Science? Don't worry!
    Every machine learning algorithm is based on matrix multiplications and commonly used linear algebra such as matrix row operations are frequently used in machine learning algorithms. To deep dive into this subject and improve your machine learning understanding, come join me on this journey.

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