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Need Help? Talk to us at +91 95402-43865 or
Need Help? Talk to us at +91 95402-43865 or
The Data Science course typically lasts for 6 to 8 months, depending on the learning path you choose (part-time or full-time). Our comprehensive curriculum is designed to accommodate different learning paces while ensuring mastery of key concepts.
No prior programming experience is required. Our course starts with the basics of programming, data analysis, and statistics to help you build a strong foundation. We then progressively dive deeper into more advanced topics such as machine learning, deep learning, and data visualization.
Upon completion of the course, you will be well-prepared for roles like:
AI Researcher
The demand for data science professionals is consistently growing, and our course will equip you with the skills needed to excel in this field.
Yes, we provide 100% job placement assistance. Our dedicated career support team will guide you through resume building, interview preparation, and job referrals to top tech companies. We also have strong partnerships with leading industry players, ensuring you have access to valuable job opportunities.
Yes, upon successful completion of our courses, you will receive a certification from Infinity Minds. This certificate is recognized by top employers and serves as proof of your skills and expertise in data science.
You will learn to work with industry-standard tools and technologies including:
Programming Languages: Python, R
Data Analysis: Pandas, NumPy
Machine Learning: Scikit-learn, TensorFlow, Keras
Data Visualization: Matplotlib, Seaborn, Tableau
Big Data: Hadoop, Apache Spark
Databases: SQL, MongoDB
Cloud Platforms: AWS, Azure
Our course is delivered through a blended learning model, consisting of:
Live sessions with industry experts
On-demand video tutorials
Hands-on coding labs and exercises
Interactive quizzes and assessments
Absolutely! You will have access to 24/7 support via:
Our goal is to ensure that you feel supported and confident throughout the learning process.
Yes, the course is designed with working professionals in mind. We offer flexible schedules, including self-paced learning and evening or weekend classes, so you can balance the course with your full-time job. The online platform also allows you to access materials at your convenience.
We provide a robust support system that includes:
Dedicated mentors who are available for 1-on-1 sessions.
Discussion forums for collaborative learning and peer support.
Live Q&A sessions to resolve any doubts.
Technical support for troubleshooting coding issues.
Yes, as part of the Data Science course, we offer interview preparation sessions that include:
Mock interviews with industry professionals
Resume building workshops
Interview coaching tailored to data science roles
Tips for handling technical assessments and coding challenges
You will need a laptop or desktop with a stable internet connection. For the software, we’ll provide all the necessary tools and resources, most of which are free or open-source (such as Python, Jupyter Notebook, and R). Additionally, we provide guides for setting up your environment.
Yes, once you enroll in the course, you will have lifetime access to the course materials, including video lectures, coding exercises, quizzes, and projects. This ensures that you can revisit and review the content anytime you need, even after completing the course.
Throughout the course, you will work on projects in areas such as:
Predictive modeling (e.g., predicting customer churn, sales forecasting)
Text analysis (e.g., sentiment analysis, text classification)
Recommendation systems (e.g., building movie or product recommendation engines)
Data visualization (e.g., creating dashboards and interactive reports)
Deep learning (e.g., image classification, speech recognition)
Yes, the course provides comprehensive training on building machine learning models. You will learn how to implement algorithms like linear regression, decision trees, random forests, SVMs, k-means clustering, and neural networks. Additionally, we cover model evaluation, hyperparameter tuning, and performance optimization.
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