Kritika Jain
Creative Data Analysis lessons with problem solving focus
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Kritika Jain
Bachelors degree
/ 55 min
About your data science tutor
I am an experienced full-stack software engineer at Nagarro, with a strong proficiency in .Net, C#, React JS, and expertise in data structures & algorithms. I have a proven track record of crafting innovative, scalable solutions and have worked on multiple applications using .Net and Outsystems(Low Code Platform), SQL Databases. In the BFSI domain, I developed and maintained web-based applications using ASP .Net and React. Additionally, I have experience as a coding instructor, teaching 100+ students online. My academic background includes a B.Tech in Computer Science from Guru Gobind Singh Indraprastha University. I am a solution-oriented problem solver, continuously learning and adapting to new technologies.
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Data engineering
Data visualization
Predictive modeling
Machine learning
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Data sciece class overview
My teaching methodology is tailored to meet the unique needs of each student. I believe in providing detailed explanations with practical examples to ensure a deep understanding of the subject matter. I strive to keep my students engaged through interactive discussions and encourage them to ask questions and seek clarifications. Additionally, I provide support with assignments, homework, and test preparation, ensuring that my students are well-prepared and confident in their abilities. My goal is to create a supportive and conducive learning environment where students can excel and achieve their academic goals.
Rated 5 stars consistently
Students appreciate how lessons simplify complex coding concepts.
Paired coding for effective learning
90% of students benefit from collaborative lessons.
Interactive debugging sessions
Students debug and improve their own code in real-time.
Your data science tutor also teaches
Data Analysis
Machine Learning
Tableau
Data Science concepts taught by Kritika
Student and Tutor reviewed essential Excel navigation shortcuts and then delved into advanced lookup and aggregation techniques. They explored the functionalities of `INDEX MATCH` for flexible data retrieval and `SUMPRODUCT` for multi-criteria data aggregation, practicing these concepts with a sales data set. The session concluded with plans for the Tutor to share the sheet for student practice and follow-up discussion.
VLOOKUP Limitations & Why INDEX MATCH is Better
INDEX MATCH for Single-Criteria Lookup
Two-Way Data Aggregation with SUMPRODUCT across Sheets
INDEX MATCH for Two-Way Lookup (Row & Column)
IFERROR for Formula Robustness
Student and Tutor had an in-depth lesson on Microsoft Outlook, covering various functionalities for email management and communication. The Student practiced creating email signatures and contact lists, and learned about search features, email categorization, calendar event scheduling, and automatic replies. They also discussed recalling messages and were advised to practice the learned features before the next session, which will focus on Excel.
Automated Communication Features
Task Management in Outlook
Calendar Management & Event Scheduling
Managing Contacts with Distribution Lists
Email Composition Essentials: CC
BCC
& Signatures
Email Organization & Prioritization
The class focused on advanced Excel financial functions, specifically reviewing the Future Value (FV) formula, introducing the Internal Rate of Return (IRR) for investment analysis, and covering common Excel error types with methods to resolve them using `IFERROR` and `ISERROR`. The Student also practiced several key Excel shortcuts for navigation and formatting. For homework, the Student was assigned to practice the learned Excel shortcuts.
Common Excel Error Types and Causes
Essential Excel Productivity Shortcuts
Handling Excel Errors with IFERROR
Internal Rate of Return (IRR)
Future Value (FV) with Initial Investment
Project Selection using IRR and Hurdle Rate
The Student and Tutor focused on mastering several key Excel functions. They practiced financial calculations using PMT (for loan payments) and FV (for future savings), as well as implementing logical IF conditions for grading and data categorization. The session also covered VLOOKUP for data retrieval, with the Student actively applying these formulas. The plan for the next session is to cover Outlook, and the Tutor will share the current spreadsheet for review.
FV Function: Projecting Future Savings
PMT Function: Calculating Loan Payments
Absolute vs. Relative Cell References ($)
Nested IF Conditions: Dynamic Grading Logic
VLOOKUP: Finding Data in Tables
The Student and Tutor reviewed the VLOOKUP function and then delved into Excel's financial functions, including PMT for calculating loan payments, FV for determining future investment values, and NPV for evaluating business investments. The Student practiced applying these formulas to various real-world scenarios. Homework was assigned to further practice the FV and NPV functions by adjusting key parameters.
VLOOKUP Refresher: Exact Match
PMT Function: Calculating Loan Payments
FV Function: Projecting Future Savings
NPV Function: Evaluating Investment Value
The Student and Tutor worked through Homework Six in R, focusing on data manipulation and statistical analysis. They imported and concatenated SAS datasets, applied complex inclusion/exclusion criteria to filter data, and created various categorical variables. The session culminated in generating a detailed "Table 1" for analysis, and the Student was advised to save the script.
Importing & Concatenating SAS Datasets in R
Applying Data Exclusion & Inclusion Criteria
Creating New Categorical Variables (Recoding)
Managing Factor Variables with Value Labels
Generating 'Table 1' Descriptive Statistics
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