Online IT Guru offers a comprehensive Data Science Course that integrates machine learning online training designed for beginners and experienced professionals. Our program provides in-depth exposure to core concepts of data science, data analytics, machine learning algorithms, statistics, Python programming, and tools widely used in the industry.
With 60+ hours of live training, practical projects, and continuous mentor support, this data science course with machine learning online prepares you for a rewarding career in data-driven domains.
Why Choose Our Data Science Course?
Our Data Science Course combines theory with hands-on practice. Below are some key reasons to choose Online IT Guru:
- Live interactive training with industry experts
- Projects using real-world datasets
- Lifetime access to LMS and recorded sessions
- Job assistance with 200+ global hiring partners
- Certification aligned with industry standards
- 24x7 learner support for queries and technical issues
Who Should Take This Data Science Course?
This program is ideal for:
- Software developers and engineers
- Data analysts and business analysts
- Professionals looking to transition into data science
- Fresh graduates aiming to build careers in data science and AI
- Project managers who want to understand machine learning applications
No prior experience in data science is mandatory, but a basic understanding of mathematics, statistics, or programming will be beneficial.
What You Will Learn in This Data Science Course
Our Data Science Course with machine learning online covers a structured curriculum, including:
Introduction to Data Science
The course begins by building a solid foundation in data science. You will understand the data science lifecycle, which represents the stages involved in executing a data science project — from identifying a problem and gathering data to cleaning, analyzing, modeling, and deploying the solution.
You will also learn about the role of a data scientist. Data scientists not only analyze data but also communicate insights that drive business decisions. This module helps you see the bigger picture — how data scientists add value in industries like healthcare, finance, e-commerce, and more.
Finally, you will get an overview of essential tools and technologies. This includes an introduction to programming languages like Python, data handling libraries, visualization tools, machine learning frameworks, and cloud platforms that modern data scientists use in their daily work.
Python for Data Science
Python is at the heart of data science because of its simplicity, readability, and rich ecosystem. In this module, you will:
Learn core Python concepts, including variables, data types, control structures (loops, conditionals), functions, and object-oriented programming. These fundamentals will enable you to write clean and efficient code.
Work with libraries such as NumPy, Pandas, and Matplotlib.
- NumPy is used for numerical computations, handling arrays and matrices efficiently.
- Pandas allows you to manipulate data structures like Series and DataFrames for easy data analysis.
- Matplotlib helps you create basic charts and graphs for data visualization.
Master data manipulation and preprocessing techniques. This includes operations like filtering, grouping, merging datasets, handling missing values, and encoding categorical variables — all necessary steps before feeding data into machine learning models.
Statistics for Data Science
Statistics forms the backbone of data science. In this module, you will gain a strong understanding of statistical methods that help describe and interpret data.
Descriptive statistics: Learn how to summarize data using measures of central tendency (mean, median, mode) and dispersion (range, variance, standard deviation).
Probability distributions: Understand how to model uncertainty using distributions such as normal, binomial, and Poisson distributions. These are critical when working with probabilistic models and making inferences from data.
Hypothesis testing: Learn how to formulate and test hypotheses using t-tests, chi-square tests, and ANOVA. This will help you determine if your findings are statistically significant.
Regression and correlation: Understand the relationships between variables using linear regression and correlation coefficients. These concepts are essential for predictive modeling and identifying patterns in data.
Machine Learning Algorithms
Machine learning is a major component of this data science course. You will gain hands-on experience with both supervised and unsupervised learning techniques.
Supervised learning:
- Linear regression for predicting continuous outcomes (e.g., house prices, sales forecasts).
- Logistic regression for binary classification (e.g., spam detection, customer churn prediction).
- Decision trees for both classification and regression tasks, useful for interpreting model decisions.
Unsupervised learning:
- Clustering techniques like k-means to group similar data points without labels.
- Dimensionality reduction methods like Principal Component Analysis (PCA) to reduce the complexity of large datasets while preserving important information.
Model evaluation and selection: Learn how to measure model performance using metrics like accuracy, precision, recall, F1-score, ROC-AUC for classification, and RMSE or MAE for regression.
Hyperparameter tuning: Use techniques like grid search and randomized search to optimize your model’s performance by finding the best parameter combinations.
This module ensures that you not only build models but also fine-tune them for better accuracy and efficiency.
Data Analysis and Visualization
This module teaches you how to explore data and communicate insights effectively.
Exploratory data analysis (EDA): Develop skills to summarize datasets, detect patterns, and identify anomalies using Python tools. EDA helps you ask the right questions and generate hypotheses for further analysis.
Visualization using Python tools: Learn to create informative visualizations using libraries like Matplotlib, Seaborn, and Plotly. These visualizations help tell a compelling story with your data.
Building interactive dashboards: Move beyond static charts to create dashboards that allow users to interact with the data — applying filters, drilling down into specific metrics, and exploring different views.
Data visualization is key in making data accessible and actionable for stakeholders.
Artificial Intelligence and Data Science Applications
In this module, you will see how artificial intelligence (AI) fits into the broader data science ecosystem.
Understand AI fundamentals, including how AI enables machines to mimic human intelligence. You will explore basic concepts like neural networks, natural language processing (NLP), and computer vision.
Learn how to integrate AI into data science workflows. This might include using pre-trained AI models for tasks like image classification, sentiment analysis, or language translation within your data science projects.
Work through real-world case studies where AI and data science are combined to solve complex problems — from automated customer support systems to fraud detection engines.
This module prepares you for the future of data science, where AI plays an increasingly important role.
SQL for Data Analytics
Structured Query Language (SQL) is essential for working with relational databases. In this module, you will:
Learn how to write SQL queries to retrieve data from databases. This includes using SELECT, WHERE, GROUP BY, JOIN, and other clauses to filter and aggregate data.
Understand data management best practices, such as indexing, normalization, and writing efficient queries that scale well with large datasets.
Practice integrating SQL queries with your Python code, allowing you to pull data directly into your data science workflows.
SQL is a critical skill because data often lives in databases, and being able to access and manipulate that data efficiently is key to any analytics or data science task.
Capstone Projects
The course culminates in a capstone project where you apply everything you’ve learned. This end-to-end project simulates a real-world business problem where you will:
Collect and preprocess data from multiple sources.
Perform exploratory data analysis and feature engineering.
Build machine learning models to solve the problem (e.g., predicting customer churn, forecasting sales, or segmenting customers).
Evaluate and fine-tune your models for optimal performance.
Visualize results and create dashboards that communicate your findings to a non-technical audience.
Optionally, deploy your model using APIs or web frameworks, and monitor its performance over time.
The capstone project ensures that you graduate from the course not only with theoretical knowledge but also with practical experience that you can showcase in your portfolio or resume.
Hands-on Projects in Data Science Course
The course includes two live projects where learners apply their knowledge:
- Predictive analytics on customer churn
- Clustering analysis for market segmentation
These projects involve real datasets, Online IT Guru ensuring you gain practical experience.
Key Features of the Data Science Course
- 60+ hours of instructor-led sessions
- 35+ assignments and exercises
- 2 capstone projects
- 18+ downloadable resources
- Lifetime access to course materials
- Certification guidance
- Job placement assistance globally
Certification and Career Support
Upon completing the Data Science Course, learners receive a certificate recognized by top employers globally. We assist with:
- Resume preparation
- Mock interviews
- Job referrals through our network of 200+ hiring partners
Why Machine Learning in Data Science?
Machine learning is at the heart of modern data science. Our course ensures:
- Strong foundation in supervised and unsupervised learning
- Experience in model building and deployment
- Capability to solve real business challenges using machine learning
The Data Science Course with machine learning online at Online IT Guru is a comprehensive program designed to give you practical skills, theoretical understanding, and the confidence to step into the data science job market. With expert mentors, real-world projects, and certification support, this course ensures that you are well-prepared for data-driven roles across industries.
Frequently Asked Questions (FAQ)
1. What is the duration of the Data Science Course with machine learning online?
The course offers 60+ hours of live sessions along with lifetime access to recorded materials.
2. Do I need coding experience for this course?
No prior coding experience is required. We start with Python basics and build from there.
3. Will I get a certificate after completing the Data Science Course?
Yes, you will receive an industry-recognized certificate upon successful completion.
4. Can I take this Data Science Course while working full-time?
Yes, we offer flexible batch timings including weekends and early morning sessions.
5. Does the course cover deep learning?
The core focus is machine learning, but the course introduces deep learning concepts as well.
6. Is job assistance provided after the course?
Yes, we offer job assistance including resume building, mock interviews, and referrals.
7. How practical is this course?
The course includes two major projects and multiple assignments for practical exposure.
8. Are the projects based on real-world scenarios?
Yes, projects use real business datasets to ensure industry relevance.
9. Can I pay the fee in installments?
Yes, installment payment options are available.
10. What tools will I learn in this Data Science Course?
You will learn Python, SQL, Pandas, NumPy, Matplotlib, and machine learning libraries like scikit-learn.