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Python Syllabus for Data Analyst

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Join the Data Science with R Training in OMR at SLA Institute, where we excel in delivering high-quality education that blends theoretical knowledge with practical skills. At SLA Institute, we focus on your career growth, offering a curriculum crafted by industry experts that covers everything from the basics of R programming to advanced data analysis and machine learning techniques. Through real-world projects, you’ll gain the hands-on experience needed to thrive in the data science field. Our dedicated team ensures that the Data Science with R Course in OMR with 100% Placement Support helps you secure a rewarding career. At SLA Institute, you’re not just gaining skills; you’re building a future. Enroll today and start your successful data science journey.

At SLA Institute, we guarantee placement in a high-paying Developer job with the support of our experienced placement officers. Our Data Science with R Course Syllabus covers all essential topics, providing you with a comprehensive understanding of Data Science with R development.

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Upcoming Batches

Hands On Training
3-5 Real Time Projects
60-100 Practical Assignments
3+ Assessments / Mock Interviews
February 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
February 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

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100% Assistance

Learning

Job-Centered Approach

Timings

Convenient Hrs

Mode

Online & Classroom

Certification

Industry-Accredited

This Course Includes

  • FREE Demo Class
  • 0% EMI Loan Facilities
  • FREE Softskill & Placement Training
  • Tie up with more than 500+ MNCs & Medium Level Companies
  • 100% FREE Placement Assistance
  • Course Completion Certificate
  • Training with Real Time Projects
  • Industry-Based Coaching By MNC IT Professionals
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Expected Criteria for Assured Placement

The following criteria help the placement team guide the candidates to get placed immediately after the course completion through SLA Institute.

  • 80% of coursework completion helps us arrange interviews in required companies.
  • 2 or 3 projects to be done for the selected course to ace the technical round effectively.
  • Ensure attending the placement training right from the first day of the selected course.
  • Practice well with resume building, soft skill, aptitude skill, and profile strengthening.
  • Utilize the internship training program at SLA for the complete technical skills.
  • Collect the course completion certificate and update the copy to the placement team.
  • Ensure your performance indicator meets the expectation of top companies.
  • Always be ready with the updated resume that includes project details done at SLA.
  • Enjoy unlimited interview arrangements along with internal mock interviews.
Have Queries? Ask our Experts

+91 89256 88858

SLA's Distinctive Placement Approach

1

Tech Courses

2

Expert Mentors

3

Assignments & Projects

4

Grooming sessions

5

Mock Interviews

6

Placements

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Python Syllabus for Data Analyst Course Syllabus

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Join our Data Science with R Training in OMR at SLA Institute to learn how to create powerful data applications. This course covers everything from basic programming to advanced data science techniques using R. You’ll gain skills in data analysis, handling large datasets, and building predictive models. With hands-on projects and expert guidance, you’ll be ready for a job in data science. Plus, our placement support will help you find a job in the field. Sign up today at SLA Institute and start your career in Data Science with R.

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
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

Project Practices on Python Syllabus for Data Analyst Training

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The Placement Process at SLA Institute

  • To Foster the employability skills among the students
  • Making the students future-ready
  • Career counseling as and when needed
  • Provide equal chances to all students
  • Providing placement help even after completing the course
On Average Students Rated The Python Syllabus for Data Analyst Course 4.80/5.0
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