So i have a very big problem.

i did my masters in english then shifted my career towards

management and did my MBA in Business analytics and systems management. but ht eproblem is i
am not getting placed and neither my resume is getting selected in off campus hiring. i am fed up.
as i dont have experience. i dont know what to do. and in masters in English the marks we used to
get was around 60% . highest being 60% and i got 59% therefore the companies who have set the
bar to 60% i am rejected before even selection . thats a issue too. i also feel i need to be adept in
both sql , r and python to enhance my skilss and every other skills which can be acquired from the
internet both for data analysis and business analyst jobs. i want to learn every bits and i want help
from u planning a precise way to do so.

Thanks for sharing that—you're definitely not alone in facing this kind of challenge. To build a tailored
learning and career strategy for you, could you please clarify a few things:

1. What is your current daily time availability to study and practice new skills?

2. Would you prefer a self-paced roadmap with online resources (like Coursera, edX, YouTube) or
structured paid certification programs?

3. Do you want to focus on a specific role (e.g., Business Analyst, Data Analyst, Data Scientist), or keep
it open for now?
4. Are you looking to prepare for internships or full-time roles?

5. Would you like the plan to include job search strategies and resume optimization as well?

Once I know this, I’ll craft a structured and effective roadmap for your upskilling and job readiness.

1. 8/9 hours
2. self paced but worthy one and also if any free good courses
3. both business and data analyst
4. full time roles
5. Yes

Thank you for the details. I’ll create a structured, self-paced upskilling roadmap focused on preparing
you for both Business Analyst and Data Analyst roles. It will include the most valuable free (or
affordable) online resources, covering essential tools like SQL, Python, R, Excel, Tableau/Power BI, and
business communication skills. I'll also incorporate job search strategies, resume optimization tips, and
a study schedule based on your 8–9 hours of daily availability.
I'll get started and share the roadmap with you shortly.

Career Transition and Upskilling Roadmap for Analyst Roles

Upskilling & Job-Search Roadmap (3–4 Months)

Printed using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API. 1/6
 Overview: The goal is to master key BA/DA skills and build a portfolio to offset limited work
experience and a 59% grade. Study ~8–9 hours/day focusing on technical tools (Python, R, SQL, Excel,
Tableau/Power BI), analytics concepts, and communication/job-search skills. Combine courses with
hands-on mini-projects. We also cover resume tactics and networking to overcome GPA concerns
blog.kudoswall.comblog.kudoswall.com.

Months 1–2: Foundations & Basics

Programming (Python & R): Start with Python fundamentals and R basics. For Python,
Harvard’s “Introduction to Data Science with Python” (edX, free to audit) covers libraries like
Pandas, NumPy, matplotlib and basic machine learningpll.harvard.edu. IBM’s “Python for
Data Science” (Coursera, free audit) also introduces Python syntax, data structures,
Pandas/Numpy, and simple web-scrapingcoursera.org. For R, Harvard’s “Data Science: R
Basics” (edX, free-to-audit) teaches R syntax, data types, visualization and analysis without
assuming prior codingclasscentral.com. Suggested practice: Kaggle’s free Python/Pandas
micro-courses and DataCamp’s (free exercises) for hands-on coding.
Databases & SQL: Learn to query databases. Udacity’s “SQL for Data Analysis” (4-week free
course) teaches SQL essentials: `SELECT` queries, `JOIN`s, aggregations, subqueries and
window functionsclasscentral.com. Practice by writing queries on sample datasets (Kaggle’s
SQL course using BigQuery is also free). Mini-project: Load a CSV into a local SQLite or
PostgreSQL, write queries to summarize the data (e.g. group totals, filter conditions).
Excel: Build spreadsheet skills for analytics. IBM’s “Excel Basics for Data Analysis” (Coursera,
free audit) covers navigation, formulas, lookup functions, pivot tables, and data cleaning
techniquescoursera.org. Also use free Udemy/YouTube courses (e.g. Udemy’s “Excel Quick
Start”) for practice. Mini-project: Create an Excel dashboard (with charts/pivots) analyzing a
simple dataset (sales or survey data).
Analytics Concepts: Review statistics and business metrics. Free resources like Khan Academy
(statistics/probability) or Coursera’s “Introduction to Data Analytics” courses help with basic
stats, data interpretation, and A/B testing concepts. Learn about common business analytics
terms (KPIs, ROI, forecasting) through blogs or short courses (e.g. edX MicroBachelors in
Business Analytics modules).

Soft Skills/BA Concepts: As an MBA, leverage your understanding of business processes. You
can supplement with a free course like Udemy’s “Business Analysis Fundamentals” or IIBA
webinars. Practice explaining technical insights in simple terms (e.g. write short reports on
your mini-project findings).

Months 3–4: Intermediate Skills & Portfolio Projects

Advanced Python/R & Data Analysis: Dive deeper into data manipulation. Learn Pandas
(Python) and dplyr/tidyverse (R) through tutorials or free courses. Perform Exploratory Data
Analysis (EDA) on real datasets: pandas/Kaggle notebooks or RStudio IDE can be used. For
example, analyze Kaggle’s Global Suicide Rates or World Happiness Report datasets to find
trends and correlationscareerfoundry.comcareerfoundry.com. Document insights in a Jupyter
or R Markdown notebook (mix code, charts, and explanations).

Printed using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API. 2/6
 Advanced SQL: Practice complex queries and performance. Use more joins, window functions,
CTEs, and database functions on larger datasets (e.g. public data on Kaggle or BigQuery public
datasets). Project: Combine multiple tables (or CSV imports) to answer a business question (e.g.
sales per region, average customer value).

Data Visualization (Tableau/Power BI): Build interactive dashboards. Take a free Tableau
course (Simplilearn’s “Free Tableau Course” covers charts, LODs, filters, dashboards
simplilearn.com) and try Tableau Public (free) to publish dashboards. For Power BI, follow
Microsoft’s free learning paths: it covers connecting data sources, using Power Query,
modeling data, and creating reportslearn.microsoft.com. Projects: Visualize your EDA results
(from above projects) in Tableau/Power BI; for example, create a COVID-19 world heatmap or
time-series chartscareerfoundry.com. Other ideas: plot most-followed Instagram accounts or
global tourism data using Python/Matplotlib or R/Shinycareerfoundry.com.

Capstone Project: Integrate skills in a larger project. For instance, scrape job postings or
financial data (using Python/BeautifulSoup or Kaggle datasets), clean/analyze it with Python/R,
store it in SQL, and present insights via Excel/BI dashboards. This end-to-end project becomes
a portfolio centerpiece showing your data story from ingestion to recommendation.

Recommended Courses & Resources

Python: HarvardX’s Introduction to Data Science with Python (edX, free audit)pll.harvard.edu;
Coursera’s Python for Data Science (IBM, free audit)coursera.org; Kaggle Learn Python track
(free).
R: Harvard’s Data Science: R Basics (edX, free audit)classcentral.com; DataCamp’s Introduction to
R (first chapters free); “Swirl” R package tutorials.

SQL: SQL for Data Analysis (Udacity, free)classcentral.com; Mode Analytics SQL Tutorial; Kaggle’s
SQL Scavenger Hunt (free).

Excel: IBM’s Excel for Data Analysis (Coursera, free audit)coursera.org; Udemy/YouTube Excel
courses for formulas, pivot tables.

Tableau: Simplilearn’s Free Tableau Coursesimplilearn.com; Tableau Public free software and
online tutorials; YouTube channel “Tableau Tim”.
Power BI: Microsoft Learn’s free Power BI paths (self-paced modules)learn.microsoft.com; edX
Analyzing and Visualizing Data with Power BI (free audit); free UDemyst tutorials.

Stats/Analytics: Khan Academy’s Statistics & Probability (free) or Coursera’s Intro to Statistics
(free audit); blogs like TowardsDataScience on EDA and analytics techniques.

Projects & Portfolio Building

Mini-Projects: For each skill learned, do a small project and share it. Examples (from data-
analytics blogs):
Web Scraping & Cleaning: Scrape data (e.g. product prices or job listings) using
Python/BeautifulSoup or Octoparse. Load and clean it in Python/R or SQL.

EDA: Use public datasets. CareerFoundry suggests analyzing Global Suicide Rates or World
Happiness data to uncover patterns (age/gender factors, country comparisons)

Printed using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API. 3/6
 careerfoundry.comcareerfoundry.com. Document your process in a well-commented
Jupyter Notebook.
Visualization: Create dashboards for storytelling. For example, build a COVID-19
outbreak dashboard (map + charts)careerfoundry.com or an interactive Instagram
influencer analysis dashboardcareerfoundry.com. Use Tableau Public or Shiny (R) to make
visuals shareable.

Industry-specific: If interested in a domain (finance, retail, health), find relevant open

datasets (Kaggle, government open data, company “data challenges”). For example,
analyze sales trends for a retail dataset using Excel/SQL and visualize in Power BI.
Capstone Project: After 2–3 mini-projects, tackle one larger project combining skills. E.g.,
“End-to-End Sales Analysis” – retrieve sales data (from scraping or public data), store in SQL,
analyze in Python (predict trends), and present insights in Power BI. This culminates your
learning and is an impressive portfolio piece.
Sharing Your Work: Publish your code on GitHub and create a portfolio website or Kaggle
profile. Include project descriptions, visuals, and key results. Career experts advise that
recruiters value evidence of practical projectsblog.kudoswall.comcareerfoundry.com.

Resume & Interview Readiness

Showcase Skills over Grades: Emphasize your hands-on skills, certifications, and projects. One
career guide notes that recruiters often value real-world skills and initiative over perfect scores
blog.kudoswall.com. On your resume, create sections like Skills or Projects at the top, rather
than focusing on GPAblog.kudoswall.com. List tools (Python, SQL, Tableau, etc.) and any
certifications (Coursera/edX certificates or DataCamp badges) prominentlyblog.kudoswall.com.
Detail Projects & Internships: Even if self-initiated, treat projects like experience: describe
objectives, tools used, and outcomes (use metrics if possible). The “Resume Whisperer”
suggests using action verbs and quantifying results (e.g. “Analyzed customer dataset to
identify 3 key buying patterns”)blog.kudoswall.com. Include any internships, volunteer work or
relevant extracurriculars to demonstrate teamwork and initiative.
Addressing Low Grades: If asked, be honest but brief about your academic percentage. Focus
on how you overcame challenges: e.g. mention simultaneous work/study or passion projects
that showcase growthblog.kudoswall.com. Use your cover letter to concisely explain any
extenuating circumstances and emphasize what you learned.

Job Search & Networking

LinkedIn Profile: Optimize LinkedIn as your online resume. Use a clear professional photo
and a headline like “Aspiring Data Analyst | Python, SQL, Tableau”linkedin.com. In your
About/Summary, highlight your transition story, top skills, and enthusiasm for analytics.
Crucially, add your projects under “Featured” or “Projects” sections with links to GitHub or live
dashboardslinkedin.com. This directly showcases your work to recruiters.
Networking: Actively engage with alumni, professors, or industry meetups. Reach out to MBA
alumni in analytics roles and ask for advice or referrals. Join LinkedIn groups and local data
science communities. Career advice emphasizes that networking and alumni contacts often

Printed using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API. 4/6
 help bypass initial screeningsblog.kudoswall.com. Attend virtual/data hackathons (e.g. Kaggle
competitions, DataHack by Analytics Vidhya) to meet peers and add experience.
Applying Strategically: For off-campus hiring, apply widely but smartly. Use LinkedIn’s job
search filters (e.g. “Entry-Level Data Analyst”) and set alerts. Target companies of all sizes: don’t
only focus on big names. Small firms and startups often have fewer formal cutoffs on grades
and value skills. Customize each resume for the role’s keywords (use your skills/projects to
match the job descriptionblog.kudoswall.com).
Interview Prep: Practice explaining your projects and the business insights you derived.
Prepare for common BA/DA questions (SQL exercises, case studies, basic statistics). Use sites
like LeetCode (for SQL/Python problems) and “Glassdoor” for company-specific interview
patterns. Mock interviews with peers or online communities can build confidence.

Weekly Schedule Template

Below is a sample 12-week schedule (4 months) allocating ~40–45 hours/week. Adjust intensity
based on progress:

Week(s) Focus / Skills Activities & Projects

- Complete Python intro (syntax, data types, lists)coursera.org
- Complete Excel fundamentals (formulas, charts)coursera.org
1–2 Python & Excel Basics
- Mini-project: Explore a small dataset (e.g. Titanic) in Excel and Python
(Kaggle Titanic tutorial)
- Udacity SQL course (SELECT, WHERE, JOIN)classcentral.com
- Harvard R Basics (variables, vectors, plots)classcentral.com
3–4 SQL & R Basics
- Project: Run simple SQL queries on a sample DB; analyze a CSV in R
(use dplyr to summarize)
- Pandas (dataframes, groupby) and NumPy (arrays)
Intermediate Python - Excel (pivot tables, VLOOKUP, data cleaning)
5–6
& Excel - Project: EDA on a real dataset (e.g. sales data) using Pandas/R and
Excel; document insights in Jupyter/notes.

- Advanced SQL (aggregations, subqueries, window functions)

Advanced SQL & - Tableau: complete a free intro coursesimplilearn.com
7–8
Visualization - Project: Use SQL to prepare a dataset; create a Tableau dashboard
(e.g. sales dashboard with maps/charts)
- Power BI: follow Microsoft Learn path (Power Query, DAX basics)
learn.microsoft.com
9–10 BI Tools & Storytelling - Polish Tableau dashboard; optionally learn R Shiny/Plotly basics
- Project: Dashboard “capstone” – combine 2–3 datasets into interactive
story (e.g., COVID trends or financial report)
- Finalize capstone project (analysis report + dashboard)
- Refine resume and LinkedIn (highlight projectslinkedin.com, skills)
11–12 Capstone & Job Prep
- Apply to 5–10 jobs daily, network online
- Practice interview questions, SQL/Python coding challenges.

Tips: Dedicate daily time to coding practice (e.g. 1–2 hours) and to portfolio work. Reserve ~5–10%
time each week for job search tasks (networking, applications). After Week 6, start adding “soft”
hours: mock interviews, resume polishing, and connecting on LinkedIn.
Sources: Reputable career guides and course descriptions inform these recommendations. For
instance, career resources emphasize showcasing skills/projects to overcome academic
shortcomingsblog.kudoswall.com. Listed courses are free or offer free audit options on

Printed using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API. 5/6
 edX/Courserapll.harvard.educlasscentral.comcoursera.org, and project ideas are drawn from
analytics blog examplescareerfoundry.comcareerfoundry.com. Networking and resume strategies
align with expert adviceblog.kudoswall.comlinkedin.com.

Sources

Printed using ChatGPT to PDF, powered by PDFCrowd HTML to PDF API. 6/6