Python for Data Analytics

Course curriculum

Python fundamentals

• Installing Python and working in Jupyter notebooks
• Variables, data types and operators
• Conditionals and loops
• Writing and reusing functions
• Modules, packages and virtual environments
• Reading from and writing to files

Data structures

• Lists, tuples, sets and dictionaries, and when each is the right choice
• Nesting structures to model real records
• List and dictionary comprehensions
• Iterating efficiently over large collections

pandas for data manipulation

• Series and DataFrame, and how pandas thinks about data
• Reading CSV, Excel and JSON sources
• Selecting with loc and iloc, and boolean filtering
• Creating and transforming columns
• groupby and aggregation for summary tables
• merge, join and concat for combining datasets
• Pivot tables, and reshaping with melt and stack

NumPy arrays

• Creating arrays, and understanding shape and dtype
• Vectorised operations instead of Python loops
• Indexing, slicing and boolean masks
• Broadcasting rules
• Aggregation and axis-wise calculation

Data cleaning

• Finding and handling missing values
• Removing duplicates and fixing inconsistent categories
• Type conversion, and parsing dates reliably
• Detecting outliers and deciding what to do about them
• String cleaning and normalisation
• Validating a dataset before trusting any analysis of it

Exploratory data analysis

• Framing the question before touching the data
• Distributions, central tendency and spread
• Correlation, and why it is not causation
• Segmenting and comparing groups
• Documenting findings in a notebook others can follow

Matplotlib visualization

• Figures, axes and the object-oriented interface
• Line, bar, scatter and histogram charts
• Labels, legends, annotation and titles
• Subplots and shared axes
• Exporting figures for reports and slides

Seaborn plots

• Statistical plots: box, violin, count and heatmap
• Pairplots and distribution comparison
• Styling, palettes and themes
• When Seaborn is the faster choice than Matplotlib

Statistical analysis

• Descriptive statistics, and what they hide
• Probability basics and distributions
• Sampling and sampling error
• Hypothesis testing and p-values, interpreted carefully
• Confidence intervals
• Simple linear regression, and reading its output

Real-world projects

• Clean and analyse a messy sales dataset end to end
• Produce an EDA report answering a defined business question
• Build a notebook that refreshes when new data arrives
• Present conclusions to a non-technical audience

Frequently asked questions

How long is the Python for Data Analytics program?

3 Months. Delivered in Online/Offline format.

Who is eligible for the Python for Data Analytics program?

Open to commerce / science graduates and working professionals; basic computer literacy required.

Does CELSE Labs provide placement assistance for Python for Data Analytics?

Placement support is available case-by-case; please contact admissions.

Is a scholarship available for Python for Data Analytics?

Yes — scholarships are available based on merit. Contact admissions for the current window.

Is an internship included in Python for Data Analytics?

Yes. The program includes a mentor-led internship with live industry projects.

Is Python for Data Analytics NSDC certified?

Yes. CELSE Labs is an NSDC-certified training partner; all programs, including this one, follow NSDC quality standards and include an industry-recognised completion certificate.

About CELSE Labs

CELSE Labs — the Centre for Experiential Learning and Skill Enhancement — is an NSDC-certified, ISO 9001:2015 accredited skill training institute and an Approved Training Partner of Medhavi Skills University. We work with commerce graduates, career changers and working professionals across Kerala who want a job in accounting, finance, compliance or data, and who need practical capability rather than another certificate.

Our training is experiential rather than lecture-based. Instead of textbook exercises, trainees operate the books, bank account, invoices and internal correspondence of simulated companies — M & M Technologies, Toy Kingdom and PetalSoft — where transactions arrive incomplete, out of order and occasionally wrong, exactly as they do in a real office. Trainees learn judgement by making decisions and correcting them, which is the part of the work that cannot be transferred by explanation.

Teaching is one-to-one, so pace follows the learner rather than a cohort. New batches start every Wednesday at both centres. Scholarships of up to 30% are available through a free online scholarship test, and our placement record includes 36+ named alumni at HDFC, ICICI, Bajaj Finserv, Kotak Mahindra, Big Basket, Ford, Amrita Hospital and JRHA Accounting in the UAE.

Visit us at Edappally in Kochi or Mavoor Road in Kozhikode, or call +91 8007 980 000 to arrange a free counselling session.