Our Python Syllabus for Data Analyst is designed to provide a comprehensive understanding of Python’s role in data analysis. The data analyst Python syllabus covers essential topics such as data manipulation with Pandas, statistical analysis, data visualization using Matplotlib and Seaborn, and machine learning techniques. By following the Python syllabus for data analyst pdf, students will gain hands-on experience in working with real-world datasets, preparing them for roles in data analytics and data science. This syllabus ensures a strong foundation in Python for effective data analysis and decision-making.
Python Syllabus for Data Analyst
4.50
(9654)
Request to Download Syllabus
DURATION
3 Months
JOB READY
Syllabus
CERTIFIED
Courses
Let's take the first step to becoming an expert in Python Syllabus for Data Analyst
Click Here to Get Started
100% Placement
Assurance
What Learning at SLA gives you
- Technology Training
- Aptitude Training
- Learn to Code (Codeathon)
- Real Time Projects
- Learn to Crack Interviews
- Panel Mock Interview
- Unlimited Interviews
- Life Long Placement Support
Python Syllabus for Data Analyst Course Syllabus
Download Syllabus
Introduction to Python for Data Analysis
- Overview of Python programming
- Setting up Python environment (Anaconda, Jupyter Notebook)
- Basic Python syntax, data types, and operators
- Control structures (loops, conditions, functions)
- Introduction to Python libraries: NumPy, Pandas
Data Handling with Pandas
- Understanding Pandas DataFrames and Series
- Importing and exporting data (CSV, Excel, SQL)
- Data cleaning: Handling missing values, duplicates, and errors
- Data transformation: Sorting, filtering, and aggregation
- Merging and joining datasets
- Data indexing and selection techniques
Data Visualization with Python
- Introduction to Matplotlib and Seaborn
- Plotting basic charts: Line, bar, histogram, scatter
- Customizing visualizations: Titles, labels, legends
- Advanced visualizations: Heatmaps, pair plots, and time series
- Data storytelling through effective visualizations
Exploratory Data Analysis (EDA)
- Techniques for data exploration and summarization
- Descriptive statistics and probability distributions
- Visualizing data distributions and relationships
- Outlier detection and handling
- Correlation analysis and feature selection
Statistical Analysis and Hypothesis Testing
- Understanding statistical concepts: Mean, median, variance
- Probability theory and distributions
- Hypothesis testing: t-tests, chi-square tests
- ANOVA and regression analysis
- Statistical significance and confidence intervals
Introduction to Machine Learning with Python
- Overview of machine learning algorithms
- Supervised learning: Linear regression, decision trees, and random forests
- Unsupervised learning: Clustering (K-means, hierarchical)
- Model evaluation: Accuracy, precision, recall, F1 score
- Model selection and overfitting
Data Wrangling and Feature Engineering
- Handling categorical data: Encoding techniques
- Feature scaling and normalization
- Feature engineering: Creating new features from existing data
- Dealing with time series data
- Advanced data wrangling techniques
Advanced Topics in Data Analysis
- Working with large datasets (Big Data concepts)
- Time series analysis and forecasting
- Natural language processing (NLP) basics
- Text mining and sentiment analysis
- Building and deploying machine learning models
Project Work and Real-World Applications
- End-to-end project on data analysis
- Real-world case studies and datasets
- Applying learned techniques to solve business problems
- Communicating results through reports and dashboards
- Capstone project with data analysis and machine learning integration
Python Tools for Data Analysts
- Introduction to Jupyter Notebooks and its features
- Working with SQL databases using Python
- Web scraping with BeautifulSoup
- Automating tasks with Python scripts
- Introduction to cloud-based tools for data analysis
Want more details about the Python Syllabus for Data Analyst Course Syllabus?
Fill out the form, and our counsellors will get in touch with you at your preferred time. You can have all your queries answered. Once you decide that SLA is the perfect fit for your training needs, our counselors will guide you through the process every step of the way.
Course Schedules
PDF Course Syllabus
Course Fees
or any other questions...
Breakdown of Python Syllabus for Data Analyst Course Fee and Batches
Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
May 2025
Week days
(Mon-Fri)
Online/Offline
2 Hours Real Time Interactive Technical Training
1 Hour Aptitude
1 Hour Communication & Soft Skills
(Suitable for Fresh Jobseekers / Non IT to IT transition)
Course Fee
May 2025
Week ends
(Sat-Sun)
Online/Offline
4 Hours Real Time Interactive Technical Training
(Suitable for working IT Professionals)
Course Fee
Save up to 20% in your Course Fee on our Job Seeker Course Series
Learn More
The SLA way to Become
a Python Syllabus for Data Analyst Expert
Enrollment
Technology Training
Coding Practices
Realtime Projects
Realtime Projects
Placement Training
Aptitude Training
Interview Skills
Interview Skills
Panel Mock
Interview
Unlimited
Interviews
Interview
Feedback
100%
IT Career
Google Reviews
Rating
4.8
1,053 Google reviews
Aswin Pandiyan
It's my genuine review i searched many institutes to do software testing course and finally landed in SLA. My counselor Bala backed me and my…
Click here for Full Review
Hari Krishnan
I am from Mechanical background. I have no command over programming before joining SLA Such a wonderful place to learn and achieve your goals.Having wonderful…
Click here for Full Review
Venkatesh
I joined SLA for python course one year back, and i got selected as software developer with decent salary, with after one year of experience,…
Click here for Full Review
Ganesan Vasu
I'm from non-IT background, but always wanted to be in IT, SLA make my dream true and I don't know how many will see this…
Click here for Full Review