Expert Top-rated Data Science tutor
Data science taught by trusted tutors in the USA
Data science tutoring with real-world context
Data science lessons delivered by USA tutors
Lessons delivered by professionals in Tucson, Fresno
Steven taught 6 days ago
The Tutor and Student reviewed conditional formatting and IF statements in Excel, including creating rules, using cell references, and inserting comments. They also practiced inserting hyperlinks to external websites and local files. The session concluded with an introduction to 'sum if', 'count if', and 'average if' functions, with plans to continue working on the what-if scenarios in the next session.
IF Statements
Conditional Formatting
Absolute and Relative Cell References
Excel Tables
Inserting Comments
Embedding Links
SUMIF
COUNTIF
Steven taught 11 days ago
The Student and Tutor discussed customizing a branded dashboard, implementing a kill switch for security, and refining the data entry process for a recruitment tool within a spreadsheet application. They focused on setting required fields and designing the user interface for screening candidates. The next session is scheduled to continue working on these functionalities.
Branding Dashboard Customization
Kill Switch Implementation
Activity Logging and Stage Tracking
Screening Form Design
Data Input Standardization
Tab Order Optimization
Steven taught 15 days ago
The Tutor assisted the Student in refining an Excel spreadsheet used to track a sales pipeline. The session covered data structuring, formula correction using `COUNTIF`, conditional formatting, and chart creation to improve data visualization. The goal was to enhance the spreadsheet's functionality and presentation for executive reporting. The Tutor sent the Student a calendar to book future sessions and to continue working with the spreadsheet.
Data Structure Importance in Excel
Excel Tables: Dynamic Data Management
Conditional Formatting for Visual Data Analysis
Countif Function: Conditional Counting
Chart Creation & Customization
Excel Formulas and Values: Pasting Special
Data Validation: Drop-down Lists
Steven taught 18 days ago
The session involved defining KPIs and metrics for a recruitment dashboard and improving the dashboard's user interface. The student and tutor discussed tracking candidate progress, identifying bottlenecks, and simplifying the user experience. The next steps include developing an activity log, exploring dashboard branding options, and refining the data structure.
Key Performance Indicators (KPIs) and Metrics in Recruiting
Importance of Standardized Data and Categorization
Activity Log for Candidate Tracking
Validating Data and Cascade Updates
User Interface (UI) Design Considerations
Steven taught 20 days ago
The student and tutor discussed the development of a sales tracking tool, including data import processes, dashboard design, and key performance indicator (KPI) selection. They outlined data structures for leads, sales, commissions, and advisor performance, and planned for data to be extracted from a CRM system. The next step involves testing data from lead statuses, along with the creation of a centralized spreadsheet to keep track of the project.
CRM Data Export and Field Customization
Mandatory Fields in CRM Journeys
Commission Tracking and Clawbacks
Data Transformation for Sales Trackers
Attendance Tracking Integration
Key Performance Indicators (KPIs) and Dashboard Design
Advisor Table and HR Data
Steven taught 23 days ago
The session focused on streamlining data import from a CRM system into Excel. The student and tutor explored methods for automating data extraction, transformation, and loading. They discussed database normalization principles and practical Excel techniques for data manipulation and error handling. The goal is to automate the process of importing data, validating it, and generating reports for various business metrics. The student was assigned the task of gathering all required data headers from their CRM system to enable future automation.
Data Export and Import
Data Normalization
Excel Import Tool with Validation
Excel Shortcuts and Techniques
Using 'IFERROR' Function
Text to Columns Feature
Data Science tutoring stats in the U.S.
Total Data Science tutors
312 Data Science tutors available
Expert Data Science tutors
82% hold a Bachelor’s degree
Data Science Industry experience
70% of tutors have worked in Data Science and Analytics
What to expect in data science
How to learn data science?
Data science is an interdisciplinary field that combines statistics, programming, and domain expertise to extract insights from data. Mastering data science can feel overwhelming due to its technical depth and vast applications. You might struggle with statistics, programming, and real-world problem-solving.
One major challenge you may face is understanding machine learning algorithms, data preprocessing, and statistical modeling. Studies show that 80% of a data scientist’s time is spent cleaning and preparing data, making it a crucial yet difficult skill to master.
Self-learning through online courses, books, and coding platforms can help you build foundational knowledge and cover theoretical concepts. However, a data science tutor can simplify technical topics, provide step-by-step explanations, and ensure hands-on practice with real-world datasets.
With data science expected to grow by 36% by 2031, acquiring the right skills is essential. Whether you’re debugging code, optimizing models, or working with large datasets, expert guidance accelerates your learning and ensures you gain practical knowledge for real-world projects.
Who should study from a data science tutor?
Struggling with complex algorithms
You may find it difficult to grasp advanced data science concepts, especially machine learning algorithms like decision trees, neural networks, and clustering. The mathematical foundations behind these topics can be complex, leading to confusion.
A data science tutor can break down these concepts into simple, digestible steps, provide real-world examples, and reinforce your learning through hands-on exercises.
Difficulty in debugging and coding efficiency
Writing optimized Python or R code, troubleshooting errors, and handling large datasets can be frustrating without proper guidance. Debugging issues, especially in large-scale projects, can take hours or even days.
A tutor helps you identify inefficiencies in your code, explains debugging techniques, and provides best practices to improve your coding workflow and accuracy.
Lack of real-world application experience
Transitioning from theoretical learning to solving practical problems can be challenging. Even after completing courses, you may struggle to apply your knowledge to real business cases or industry projects.
A data science tutor bridges this gap by offering guided projects, industry case studies, and personalized feedback to help you develop real-world problem-solving skills.
How to select the right data science tutor?
Educational Qualification
Look for data science tutors with a background in data science, statistics, or machine learning. If you’re an advanced learner, you’ll benefit from a tutor with industry experience.
Experience & Teaching Approach
A tutor with hands-on experience in AI, finance, or healthcare can help you gain practical insights.
Student Reviews & Testimonials
Choose private data science tutors with positive feedback from learners who have successfully transitioned into data science careers.
Affordability & Value
Data science tutors charge between $50 to $200 per hour, depending on experience and level of mentorship.
Finding the right data science tutoring can make a significant difference in your journey to mastering data science. Wiingy offers expert-vetted tutors at an affordable rate of $28 per session, ensuring high-quality learning at the best value. With structured guidance, personalized learning, and hands-on experience, you can develop the skills needed for a successful career in data science.
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