Python Programming & Data Analytics - 120 Hours Internship Training Program
Join 120 Hours Python Programming & Data Analytics Internship Training Program at NextVision Infotech. Master Python, Pandas, NumPy, SQL, Power BI, Statistics, Machine Learning Basics. ₹1499 only with certificate.
Program Description
Skills You'll Learn
Projects You'll Work On
Complete Python Programming & Data Analytics Project (120 Hours)
Students will complete a full Python and data analytics project including data cleaning, Python analysis, visualization, SQL queries, Power BI dashboard, statistical analysis, machine learning basics, research report, and complete project documentation.
Program Modules
W1-2: Weeks 1-2: Python Programming Fundamentals
Python introduction, installation, Python syntax, variables, data types, operators, control flow, loops, functions, parameters, arguments, return values, scope, lambda functions, map, filter, reduce, lists, tuples, sets, dictionaries, list comprehensions, dictionary comprehensions, string manipulation, regular expressions, file handling, error handling, exception handling, modules, packages, virtual environments, pip, object-oriented programming - classes, objects, inheritance, polymorphism, encapsulation, abstraction, magic methods, decorators, generators, iterators, context managers, Python standard library.
W3-4: Weeks 3-4: Advanced Python and Data Analytics
Advanced Python, type hints, dataclasses, Pydantic, async/await, asyncio, threading, multiprocessing, NumPy fundamentals, NumPy arrays, indexing, slicing, reshaping, broadcasting, mathematical operations, statistical operations, linear algebra, Pandas fundamentals, Series, DataFrame, data loading - CSV, Excel, JSON, SQL, data inspection, data selection, filtering, sorting, grouping, aggregation, merging, joining, concatenation, pivot tables, time series analysis, data cleaning, handling missing values, outlier detection, feature scaling, encoding categorical variables, data transformation.
W5-6: Weeks 5-6: Data Visualization and SQL
Matplotlib fundamentals, line plots, bar plots, histograms, scatter plots, pie charts, box plots, heatmaps, Seaborn, statistical data visualization, Plotly, interactive visualization, data visualization best practices, dashboard design principles, SQL fundamentals, relational databases, SELECT statement, WHERE clause, ORDER BY, GROUP BY, HAVING, JOINs - inner join, left join, right join, full join, subqueries, CTEs, window functions, aggregate functions, string functions, date functions, case statements, data manipulation, data definition, views, stored procedures, database design, normalization, indexing, query optimization, SQL with Python - SQLite, MySQL, PostgreSQL, SQLAlchemy.
W7-8: Weeks 7-8: Power BI and Business Intelligence
Introduction to Power BI, Power BI Desktop, Power BI Service, connecting to data sources - Excel, CSV, SQL Server, MySQL, PostgreSQL, web, APIs, Power Query, data transformation, data cleaning, data profiling, combining queries, appending queries, merging queries, data modeling, star schema, snowflake schema, relationships, cardinality, calculated columns, calculated tables, date tables, hierarchies, DAX, DAX syntax, DAX functions - aggregate functions, iterator functions, filter functions (CALCULATE, FILTER, ALL), time intelligence functions, data visualization principles, chart selection, bar charts, column charts, line charts, pie charts, maps, KPI cards, slicers, filters, bookmarks, buttons, drill-through, drill-down, dashboard design, layout design, color theory, data storytelling.
W9-10: Weeks 9-10: Statistics and Machine Learning Basics
Statistics for data analytics, descriptive statistics - mean, median, mode, standard deviation, variance, range, percentile, quartile, inferential statistics, hypothesis testing, t-test, chi-square test, ANOVA, correlation analysis, regression analysis, linear regression, multiple regression, time series analysis, trend analysis, forecasting, machine learning basics, supervised learning - linear regression, logistic regression, decision trees, random forests, unsupervised learning - k-means, model evaluation, cross-validation, scikit-learn, Jupyter Notebook, Google Colab.
W11-12: Weeks 11-12: Research, Live Projects, and Certification
Research methodology, research design, literature review, data collection, data analysis, research report writing, live projects - sales dashboard, HR dashboard, financial dashboard, marketing dashboard, customer segmentation, predictive analytics, data automation, web scraping, API integration, research report, complete documentation, resume building, interview preparation, portfolio building, LinkedIn optimization, viva-voce examination, feedback session, certificate distribution.
Key Features
What You'll Learn
Examination Details
| Exam Name | Full Marks | Pass Marks |
|---|---|---|
| Module-wise Assessment | 100 | 40 |
| Project Evaluation | 100 | 50 |
| Research Report | 100 | 50 |
| Final Presentation | 50 | 25 |
| Viva-Voce | 50 | 25 |
Internship Details
Prerequisites
- Pursuing or completed B.Tech/B.E./BCA/MCA/B.Sc./B.Com/MBA in any discipline
- Basic knowledge of programming concepts
- Basic knowledge of Excel
- Interest in Python and data analytics
Who Can Enroll ?
- B.Tech students (1st to 8th semester)
- BCA/MCA students
- B.Sc. students
- B.Com students
- MBA students
- Python developers
- Data analytics aspirants
- Working professionals wanting to upskill
- Students from all over India seeking advanced practical training
Certificate Preview
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