Best Data Science Course In Nagpur With Placement Guarantee.
Zappcode Academy offers a data science course in Nagpur where you can work on live projects and secure an internship.
- Expert Trainer
- Affordable Fees
- Placement Opportunities
- Hands on Training
- Flexible Timings
- Industry Based Training
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Overview Of Data Science Course In Nagpur
Data science has attracted many due to the combination of statistics and programming with domain knowledge. And how long it will take to explore this immense territory? The data science course duration depends on many factors such as personal and professional background and learning speed as well as the pace of study in the selected program.
Fast Track (Bootcamps: Intermediate (3-6 months): Suitable for people with some programming experience who want a crash course. Courses in data science are time focused and condensed therefore the student has to be fully involved.
Comprehensive Coverage (Master’s Degrees: 1-2 years): The Master’s degree level is the best choice for those who want to learn data science theory and practice. This path provides a rigid classroom and lab setting and the opportunity to participate in research.
Benefits Of Choosing Data Science
Choosing a career in data science brings several clear benefits, especially when starting with a data science course in Nagpur. Here’s what makes this field a strong choice:
- Good Job Opportunities: There is a high demand for data scientists, leading to strong job prospects and good salaries.
- Meaningful Work: You will work on important projects, look at data trends, and make decisions that can impact various fields.
- Ongoing Learning: The field encourages you to keep learning and stay updated with new tools and methods.
- Rewarding Challenges: The work involves solving practical problems and helping with key decisions, making the career both engaging and fulfilling.
Our data science course in Nagpur can set you on a path to a successful and satisfying career in this growing field.
Why To Choose Our Data Science Course In Nagpur
Choosing a career in data science comes with some great benefits. By taking our data science course in Nagpur, you’ll be stepping into a field that’s growing quickly. There’s a high demand for skilled data scientists, which means solid job prospects and good pay. In this career, you’ll get to work on interesting projects, analyze trends, and make decisions based on data that can really make a difference across different industries. Plus, it’s a role that keeps you learning and adapting, so you’re always in touch with the latest tools and methods. With a focus on solving real problems and helping make strategic decisions, a career in data science can be both fulfilling and exciting.
Highlights of Zappcode Academy
Why To Choose Our Data Science Course In Nagpur
Get 100% Job Assistance By Enrolling In Certified Data Science Course In Nagpur
Placement Initiative for our Students
Zappcode Academy's Job Assistance Program
Zappcode Academy’s assistance program offers guidance on interview techniques, common questions, and how to effectively showcase your data science skills. We equip our students with the knowledge and confidence needed to succeed in data science interviews and beyond. This support is especially valuable for those seeking a data science course in Nagpur .
- Assured Training and Placement
- Placement Guidance
- Resume Building
- Mock interviews
- Drive Exams
- Provide Internship in Zappkode Solutions
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Our Students Placed and Working with Companies
ENQUIRY FOR Data Science COURSE FEES
Data Science Course Curriculam
Our Curriculum Program Covers Basic To Advance Level Content on Data Science
- Introduction to Data Analytics, types of data, data lifecycle
- Introduction to Excel: Interface, basic functions, and formulas
- Data entry, formatting, and basic data manipulation
- Basic formulas and functions (SUM, AVERAGE, COUNT)
- Working with ranges, cells, and data validation
- Advanced formulas (VLOOKUP, HLOOKUP, INDEX, MATCH)
- PivotTables and PivotCharts
- Conditional formatting and data visualization
- Advanced data analysis (What-If Analysis, Solver)
- Macros and basic VBA
- Introduction to databases, types of databases (relational vs. non-relational)
- Basics of SQL: SELECT, FROM, WHERE
- GROUP BY, HAVING, ORDER BY
- JOINs (INNER, LEFT, RIGHT, FULL)
- Subqueries and nested queries
- Window functions (ROW_NUMBER, RANK, DENSE_RANK)
- Common Table Expressions (CTEs)
- Indexing and performance optimization
- Data modification (INSERT, UPDATE, DELETE)
- Working with dates and strings
- Setting up Python environment, basics of Python programming
- Data types, variables, and basic operations
- Control structures (for, while, break, continue)
- Conditional Statements (If, Elif, Else)
- List & Arrays
- Tuple & Sets
- Dictionaries
- Comprehensions
- Defining & Calling Functions, types of functions
- Lambda, *args & **kwargs
- Defining classes and creating objects
- Inheritance and polymorphism
- Encapsulation & Abstraction
- Introduction to NumPy: Arrays and basic operations
- Array manipulation and mathematical functions
- Introduction to Pandas: DataFrames and Series
- Data manipulation (filtering, sorting, grouping)
- Pandas: Merging, joining, and concatenating Data Frames
- Introduction to Matplotlib: Basic plots (line, bar, scatter)
- Customizing plots (labels, titles, legends)
- Introduction to Seaborn: Statistical plots (boxplot, violin plot)
- Advanced visualization techniques
- Plotly: Interactive visualizations
- Understanding the dataset and basic statistics
- Data cleaning and preprocessing
- Univariate and bivariate analysis
- Handling missing values and outliers
- Data visualization for insights
- Basics of descriptive statistics
- Probability theory and distributions
- Hypothesis testing and confidence intervals
- Correlation and regression analysis
- Statistical significance and p-values
- Introduction to Power BI
- Importing Data
- Data Modeling
- Creating relationships between tables
- Explore DAX (Data Analysis Expressions) language
- Data Visualization
- Customize and format visualizations
- Data transformation techniques using Power Query Editor
- Parameters and functions to make your queries dynamic
- Handling errors and exceptions in data transformations
- Power BI Service and Sharing
- Introduction to Machine Learning
- Basic Statistics and Probability
- Linear Algebra and Calculus
- Data Preprocessing
- Regression Algorithms – Linear regression, Polynomial regression
- Classification Algorithms – Logistic regression, KNN, SVM
- Decision Trees and Ensemble Methods – Decision Trees, Random Forest, Gradient Boosting
- Clustering Algorithms – K-Means clustering, Hierarchical clustering, DBSCAN
- Dimensionality Reduction
- Model Evaluation and Selection – Cross-validation techniques, Hyperparameter tuning: Grid Search, Random Search
- Introduction to deep learning: Multi-layer Perceptrons (MLP)
- Overview of frameworks: TensorFlow, Keras, PyTorch
- Text preprocessing: Tokenization, stemming, lemmatization
- Time series forecasting
- Project setup: Identifying a real-world dataset and objectives
- Data cleaning: Handling missing values and inconsistencies
- Data transformation: Feature scaling and encoding
- Exploratory data analysis: Identifying patterns and insights
- Project review and documentation
- Project setup: Identifying a real-world dataset and objectives
- Data visualization: Creating effective charts and plots
- Advanced visualization techniques: Interactive and animated plots
- Building a comprehensive dashboard
- Project review and documentation
- Project Setup and Dataset Identification
- Data Cleaning
- Data Transformation
- Exploratory Data Analysis (EDA)
- Model Selection and Training
- Project Review and Documentation
- Capstone project: Integrating all learned skills
- Data collection, cleaning, and preprocessing
- Data analysis, visualization, and modeling
- Finalizing the project and preparing a presentation
- Presenting the capstone project and receiving feedback
- Introduction to Git
- Basic Git Workflow
- Branching and Merging
- Remote Repositories with GitHub
- Collaboration on GitHub
- Forking and Pull Requests
- Resume Building: Crafting an effective resume highlighting technical skills
- Mock Interview Practice: Behavioral and Technical questions
- Creating a LinkedIn Profile: Optimizing for job search
- Networking Strategies: Connecting with professionals in the industry
- Job Search Strategies: Applying for positions, leveraging LinkedIn
- Portfolio Development: Showcasing projects on GitHub, LinkedIn
- Job Ready Preparation: Final review, polishing resume and online profiles
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Frequently Asked Questions
Data science is one of the mélange fields which involve statistics, computer science, and domain knowledge, to convert all the data into meaningful insights. It has tasks from data collection, getting information ready, modeling, analyzing, and interpretations that help solve today’s problems.
When we discuss data science, it should be noted that it can be multiple career practices. Various attractive options are available among which data analyst, machine learning engineer, data scientist, business intelligence analyst, and data architect are the most popular. The particular path of the degree relies on one’s interests and specialization.
The point here is that a degree in data science, statistics, computer science, or the one related is not necessarily a requirement. Now, most of the data scientists belong to different disciplines. Nevertheless, a solid foundation encompassing math, statistics, and programming has also been a must, which you can acquire through boot camps, other online courses, or self-study.
There will be a very high demand for data scientists in the next few years. For this reason, training data scientists is an extremely important task for educational institutions. The growing massive role of data across industries will mean that the services of data science experts are highly in demand, and these professionals are likely to experience enhanced career prospects.
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