Corporate Data Science Training in Delhi: A Complete Guide

BUSINESS PROBLEM STATEMENT (one per use case)

Team        : Customer success
Problem     : Customers cancel without warning
Decision    : Whom should we call each week?
Data we have: 2 years of usage logs, tickets, billing
Owner       : Head of customer success
Success looks like:
  - A weekly list of at-risk accounts the team trusts
  - The team can explain why an account is flagged

Skills needed: SQL, pandas, classification basics,
               model evaluation, simple dashboards

DATA READINESS CHECK (before training starts)

[ ] We know where the data lives (DB, CRM, files)
[ ] Trainees can get read access, or a safe sample
[ ] Personal data can be masked for training use
[ ] One person can explain what each column means
[ ] At least a year of history for forecasting cases

If most boxes are empty, add a data-preparation
module before modelling — or use public datasets
for practice and company data for the final project.

SKILLS MATRIX (rate 0-3, verify with a short task)

Skill                     Person A  Person B  Person C
------------------------  --------  --------  --------
SQL queries and joins        _         _         _
Python basics                _         _         _
pandas data cleaning         _         _         _
Statistics (mean, spread,    _         _         _
  hypothesis testing)
scikit-learn modelling       _         _         _
Model evaluation metrics     _         _         _
Communicating results        _         _         _

0 = none  1 = basic  2 = independent  3 = can teach
Track: score mostly 0-1 -> Domain/Analyst foundation

# 20-minute practical check for the analyst track
import pandas as pd

df = pd.read_csv("orders_sample.csv", parse_dates=["order_date"])

# 1. How many orders per month?
monthly = df.groupby(df["order_date"].dt.to_period("M")).size()
print(monthly)

# 2. Which region has the highest average order value?
print(df.groupby("region")["amount"].mean().sort_values(ascending=False).head(1))

# 3. How many rows have a missing customer_id?
print(df["customer_id"].isna().sum())

# Can finish all three alone -> analyst track
# Struggles with groupby     -> foundation track first

SAMPLE PROGRAMME OUTLINE (analyst track, adjust with trainer)

Phase 1  Python and pandas refresher, SQL practice
Phase 2  Statistics and exploratory analysis
Phase 3  Supervised learning with scikit-learn
         (regression, classification, evaluation)
Phase 4  Use-case project on company data
Phase 5  Presentation to business owner + review

Rhythm  : 2 sessions per week, 2-3 hours each
Between : 2 hours of assignments per week
Blocked : no sessions in month-end closing week

IMPACT REVIEW (about 8 weeks after training)

Use case            : Churn risk list
Project finished?   : Yes / No
Used by business?   : Weekly / Occasionally / Not used
Who uses it         : ____________________
What changed        : ____________________
Blockers            : data access / time / skills / other

Trainee feedback (1-5):
  Relevance to my job : _
  Confidence to apply : _

Next step: extend / fix data / refresher / close

CORPORATE TRAINING BRIEF

Company / team   : ______________________
Business goals   : 1. ______  2. ______
Participants     : ___ leaders  ___ analysts  ___ engineers
Current level    : from skills audit (attach matrix)
Format preferred : classroom / live online / blended
Location         : Laxmi Nagar / Noida / online
Schedule limits  : days, times, blackout weeks
Tools in use     : Python, SQL DB, BI tool, cloud
Data for projects: company data / masked / public
Success measure  : projects used by business team

QUESTIONS TO ASK ANY TRAINING PARTNER

1. Can modules be adjusted for each role track?
2. Who are the trainers and what project work have they done?
3. How much of each session is hands-on lab work?
4. Can trainees work on our use cases with masked data?
5. How are assignments reviewed and feedback given?
6. What happens if someone misses sessions?
7. Can we attend a demo session first?
8. What does the commercial proposal include and exclude?

Get answers in writing and compare like for like.

ROLLOUT CHECKLIST

Before
[ ] Business owner named for each use case
[ ] Skills audit done, tracks assigned
[ ] Laptops: Python, Jupyter, libraries installed
[ ] Data access or masked samples ready
[ ] Managers have blocked calendars

During
[ ] Attendance and assignment tracking
[ ] Mid-point check-in with trainer and managers

After
[ ] Final project presentations
[ ] Impact review scheduled ~8 weeks later