Data Analyst
Course in Hyderabad
A role-focused path through the Data Science Certification Program
This role course arranges the Data Science programme for a data analyst who wants statistics and Python behind the reports. You start with SQL and Pandas, add statistics and exploratory analysis, learn to chart findings clearly, and finish with an introduction to machine learning.
- SQL queries
- Pandas cleaning
- Hypothesis testing
- Correlation analysis
- Data profiling
- Matplotlib and Seaborn
- Chart selection
- Clustering basics
Same duration and fees as the Data Science programme.
What a Data Analyst does
A data analyst finds out what happened in the business and why, using data. A sales head wants to know why one region fell behind last quarter. The analyst pulls the numbers, checks them, compares groups and reports back with a chart and a short explanation. From a data science angle, the analyst also uses statistics to check that a difference is real, and Python to repeat the analysis quickly when new data arrives.
A normal week starts with a question from someone in another team. You write SQL to get the data, clean it in Pandas, profile it, and look at distributions and correlations in a notebook. Then you choose a chart, write two or three sentences on what stands out, and share it. Now and then you fit a simple regression or a clustering to see whether a hidden pattern is worth reporting.
Data analysts are needed almost everywhere numbers are produced: banks, retail, healthcare, telecom, logistics, education and government teams, as well as IT services and start-ups. The role matters because most decisions are made by people who are not data specialists. They need someone who can check the numbers, spot mistakes early and explain results honestly. It is also a common first step toward data science.
What you will be able to do
- Write SQL with SELECT, JOIN, GROUP BY and subqueries to extract data from a relational database.
- Clean a real dataset in Pandas by handling missing values, duplicates and outliers.
- Calculate descriptive statistics and interpret a hypothesis test and a confidence interval.
- Explore a dataset with univariate, bivariate and multivariate analysis and summarise what you find.
- Choose the right chart for a question and build it in Matplotlib or Seaborn.
- Arrange several visuals into a small dashboard that tells one clear data story.
- Fit a basic classification or clustering model and explain what its metrics mean.
- Document your work in a notebook so another person can follow and repeat it.
Who this course is for
Fresh graduate
You finished a degree and want an entry job that uses data. You begin with SQL and Python, then build up statistics and charts, so you finish with several documented projects to talk about at interviews.
Excel user in operations, finance or sales
You already maintain reports and trackers in Excel. This course moves you to SQL and Python for larger data, and adds the statistics that let you defend a number when someone questions it.
IT support or testing engineer
You are used to logs, tickets and systems. Analysis roles are a natural next step, and the SQL and Pandas modules turn your habit of tracing problems into a habit of finding patterns in data.
Career switcher from a non-IT field
You come from teaching, commerce, HR or another field and want a technical career. The course starts from the basics, so you can learn at a steady pace and build a portfolio as you go.
What you will learn as a Data Analyst
These are the Data Science programme modules that matter most for this role, in the order that suits it. Every topic, tool and lab below is part of the programme syllabus.
Data Wrangling (SQL + Cleaning)
Module 3 · 20 HrsAnalysis starts with getting the right rows. Concentrate on SELECT, JOIN and GROUP BY, then on handling missing values, outliers and duplicates, so the table you analyse is one you have checked yourself.
See the full module →What you study
- Relational databases & SQL fundamentals
- SELECT, JOIN, GROUP BY & subqueries
- Handling missing values & duplicates
- Outlier detection & data standardization
- Merging, reshaping & pivoting datasets
- Building clean, analysis-ready datasets
Tools you use
SQLMySQL / PostgreSQLPandasHands-on lab
Write SQL queries using SELECT, JOIN, GROUP BY and subqueries against a real database.
Python Programming
Module 2 · 30 HrsPython lets you repeat an analysis in seconds instead of redoing it by hand. Focus on data structures, file handling, NumPy and Pandas, and on writing a script that loads, cleans and summarises a dataset.
See the full module →What you study
- Lists, dictionaries, tuples & sets
- File handling & exception handling
- NumPy for numerical computing
- Pandas for data manipulation
- Writing clean, reusable Python code
Tools you use
PythonJupyter NotebookPandasNumPyHands-on project
Python Data Processing Script. Build a Python script that loads, cleans and summarises a real-world dataset using NumPy and Pandas.
Mathematics for Data Science
Module 1 · 20 HrsStatistics is what keeps an analyst from reporting noise as news. Concentrate on distributions, hypothesis tests, confidence intervals and correlation, and practise saying in one sentence what each result means.
See the full module →What you study
- Probability & statistics fundamentals
- Descriptive statistics & distributions
- Hypothesis testing & confidence intervals
- Correlation & regression basics
- Statistical inference for data decisions
Tools you use
SciPyExcelHands-on lab
Run a hypothesis test and interpret a confidence interval on sample data.
Exploratory Data Analysis (EDA)
Module 4 · 20 HrsExploration is the heart of analyst work. Practise looking at one, two and many variables, profiling data, spotting anomalies and outliers, and writing your early findings so a colleague can follow them.
See the full module →What you study
- Univariate, bivariate & multivariate analysis
- Summary statistics & data profiling
- Identifying patterns, trends & anomalies
- Correlation analysis between variables
- Outlier detection techniques
- Communicating early insights clearly
Tools you use
PandasJupyter NotebookPandas ProfilingHands-on project
EDA & Visualization Project. Explore a dataset end-to-end and present findings through Matplotlib and Seaborn visuals.
Data Visualization (Matplotlib / Seaborn)
Module 5 · 20 HrsA good chart saves a page of explanation. Learn which plot fits which question, how to customise Matplotlib figures, when Seaborn heatmaps help, and how to put several visuals together in one story.
See the full module →What you study
- Line, bar, scatter & histogram plots
- Seaborn statistical plots & heatmaps
- Multi-panel & faceted visualizations
- Storytelling with data
- Choosing the right chart for the data
- Building simple visualization dashboards
Tools you use
MatplotlibSeabornHands-on lab
Assemble a small visualization dashboard that tells a coherent data story.
Machine Learning Fundamentals
Module 7 · 30 HrsTreat this as an introduction, not a specialisation. Learn supervised and unsupervised learning, simple regression and clustering, and how to read evaluation metrics, so you can try a model and explain it.
See the full module →What you study
- Supervised vs. unsupervised learning
- Regression: linear & logistic
- Clustering: K-Means & hierarchical clustering
- Model evaluation: accuracy, precision, recall, F1
- Train-test split, cross-validation & overfitting
- Feature scaling & feature selection
Tools you use
Scikit-learnPandasJupyter NotebookHands-on lab
Apply K-Means and hierarchical clustering to segment unlabeled data.
Where a Data Analyst course can take you
First jobs
Data Analyst (Trainee), Data Analyst (Junior), Reporting Analyst and Research Analyst (Junior) are the entry titles the programme points to. Junior Business Analyst is another way in.
Broader analyst roles
With experience you can move to Insights Analyst, Product Analyst or Business Intelligence Analyst, owning a topic area or the dashboards for a team, with statistics behind your numbers.
Toward data science
Because you also learn machine learning basics, Data Analyst (ML-focused) and Junior Data Scientist roles are within reach. From there the programme's career map continues to Senior Data Scientist and Data Science Team Lead.
Certifications the programme prepares you for
- Google Data Analytics Professional Certificate
- IBM Data Science Professional Certificate
- Microsoft Certified: Power BI Data Analyst Associate
Data Analyst course, quick answers
Do I need to know statistics to become a data analyst?
Basic statistics helps a lot. It lets you tell a real change from random noise, so you avoid reporting differences that mean nothing. The maths module covers averages, distributions, hypothesis testing and correlation with real data, so you can start without prior knowledge.
Should a data analyst learn Python as well as SQL?
It helps, especially on a data science route. SQL gets data out of databases, while Python with Pandas cleans, explores and charts it, and lets you rerun work on new files quickly. The course teaches both from the beginning.
What is the difference between a data analyst and a data scientist?
A data analyst mainly explains past and present data, while a data scientist also builds models that predict. The skills overlap a lot. This path keeps you on the analyst side and adds machine learning basics so the next step stays open.
Will I learn machine learning as a data analyst?
You learn the basics: regression, classification, clustering and how to evaluate them. That is enough to try a simple model and explain it. You do not need it for every analyst role, but many teams value an analyst who can go one step further.
How do I build a portfolio as a data analyst?
Keep the work from the labs and projects, such as a cleaned dataset, an exploratory analysis and a set of charts. Put each one in a notebook on GitHub with a short summary of the question, the method and the finding.
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Other roles in the Data Science programme
Part of the Advanced Data Science Certification Program
Every role course follows the same Data Science programme, with the same modules, labs, projects and internship. See the full syllabus and every module.