Data Science Course in Lucknow
Duration : 3 Months (2 Hours Daily) / 6 Months (1 Hour Daily)
The Data Science Course in Lucknow offered by Gyandeep Edutech is designed to help students, graduates and working professionals master the complete data science lifecycle. In today’s data-driven world, organizations generate massive amounts of data, creating a growing demand for professionals who can analyze information, identify trends and make data-driven business decisions. This course covers everything from statistics and data handling to machine learning and real-world project implementation.
Students learn Python Programming, NumPy, Pandas, Matplotlib, Seaborn and SQL as part of the Data Science Course in Lucknow. The course provides a strong foundation in statistical analysis, data visualization, data processing and machine learning techniques. Learners gain practical experience in working with real-world datasets, identifying patterns, building predictive models and making data-driven decisions using industry-standard tools and technologies.
The Data Science Course at Gyandeep Edutech includes hands-on training, industry-oriented projects, machine learning algorithms, business intelligence concepts and artificial intelligence fundamentals. Students build practical portfolios and work on real-world case studies that prepare them for careers in Data Science, Data Analytics, Machine Learning and Artificial Intelligence.
Data Science Course Syllabus and Learning Outcomes
| Data Science Course Syllabus | |||
| No. | Course Details | ||
|---|---|---|---|
| 1 | Introduction to Data Science and Analytics | ||
| 2 | Python Programming Fundamentals | ||
| 3 | Statistics and Probability for Data Science | ||
| 4 | Data Collection and Data Cleaning Techniques | ||
| 5 | SQL and Database Management | ||
| 6 | Data Analysis using Pandas | ||
| 7 | Numerical Computing using NumPy | ||
| 8 | Data Visualization using Matplotlib and Seaborn | ||
| 9 | Exploratory Data Analysis (EDA) | ||
| 10 | Machine Learning Fundamentals | ||
| 11 | Supervised Learning Algorithms | ||
| 12 | Unsupervised Learning Algorithms | ||
| 13 | Model Evaluation and Performance Metrics | ||
| 14 | Artificial Intelligence Fundamentals | ||
| 15 | Business Intelligence and Data Reporting | ||
| 16 | Real-World Data Science Case Studies | ||
| 17 | Industry-Oriented Data Science Projects | ||
| 18 | Career Preparation and Portfolio Development | ||
