Become a Master in Data Science
- Open to graduates
- No prior coding experience
- 100 % Placement Assistance
- 6 months live training
- 8 industry-based projects
- Continuous support till placement
- Life-time career support
- 00Days
- 00Hours
- 00Minutes
- 00Seconds
About Us
- Transformative tech-driven institute for aspirants and professionals.
- High-quality, industry-tailored programs.
- Holistic learning with practical application.
- Nurturing and supportive for skill development
- Career assistance at every stage.
- Industry-relevant, updated for tech trends. role in addressing challenging issues.
- Hands-on for real-world skills.
Steps to become a Data Scientist
1.1 Mathematics
Statistics and Probability
Limit and Derivative
Calculus
Linear algebra
1.2 Data
Data Lake, Data Ware house, Data base
Sourcing data
Understanding Data
2.1 SQL / BQ / DB2 / Oracle
Proficient in querying and manipulating data using SQL, BigQuery, DB2, and Oracle databases.
2.2 Pyhon/R for Data Analysis
Python/R for data analysis: Skilled in using Python and R programming languages for in-depth data analysis and exploration.
3.1 Excel, Tableau, Power BI, Looker, Alteryx
Experienced in creating compelling visualizations using tools like Excel, Tableau, Power BI, Looker, and Alteryx.
Proficient in designing interactive dashboards for data-driven decision-making.
Skilled in transforming complex data sets into clear and insightful visual narratives.
3.2 Python Libraries for Visualization
Seaborn: Utilizes Seaborn, a Python data visualization library, for creating informative and attractive statistical graphics.
Matplotlib: Proficient in Matplotlib, a versatile plotting library in Python, for generating a wide range of visualizations.
4.1 Supervised Learning
Algorithms like linear regression, logistic regression, decision trees, random forest, support vector machines, K-nearest neighbours.
4.2 Unsupervised Learning
Algorithms like K-means clustering, hierarchical clustering, DBSCAN, principal component analysis (PCA).
4.3 Specialized Areas
Techniques like natural language processing, image processing, time series analysis, anomaly detection, recommendation systems.
Deep Learning: Learn to use neural networks, convolutional neural networks (CNN),recurrent neural networks (RNN),Long short-term memory (LSTM), generative adversarial networks (GANs).
Reinforcement Learning: Basics of reinforcement learning, Q-learning.
6.1 Big Data Technologies
Understand the basics of big data tools like Hadoop, Spark.
6.2 Cloud Platforms
Cloud platforms like AWS, Google Cloud, Microsoft Azure.
Familiarize with cloud-based ML services and data warehousing services.
Communicate technical results to non-technical stakeholders.
Understand the basics of industries where you wish to apply data science.
Effective communication skills and personality development.
CV preparation.
Interview Preparation.
Industry level multi domain expertise.
Hands on experience on multiple projects.
Real data driven projects on multiple domains.
Modelling and Reporting
Use case preparation to Presentation
Work on projects that give you real-world challenges to solve.
Share your work on GitHub, Kaggle or on a blog to showcase your skills.
1.1 Mathematics
- Statistics and Probability
- Limit and Derivative
- Calculus
- Linear algebra
1.2 Data
- Data Lake, Data Ware house, Data base
- Sourcing data
- Understanding Data
2.1 SQL / BQ / DB2 / Oracle
- Proficient in querying and manipulating data using SQL, BigQuery, DB2, and Oracle databases.
2.2 Python/R for Data Analysis
- Python/R for data analysis: Skilled in using Python and R programming languages for in-depth data analysis and exploration.
3.1 Excel, Tableau, Power BI, Looker, Alteryx
- Experienced in creating compelling visualizations using tools like Excel, Tableau, Power BI, Looker, and Alteryx.
- Proficient in designing interactive dashboards for data-driven decision-making.
- Skilled in transforming complex data sets into clear and insightful visual narratives.
3.2 Python Libraries for Visualization
- Seaborn: Utilizes Seaborn, a Python data visualization library, for creating informative and attractive statistical graphics.
- Matplotlib: Proficient in Matplotlib, a versatile plotting library in Python, for generating a wide range of visualizations.
4.1 Supervised Learning
- Algorithms like linear regression, logistic regression, decision trees, random forest, support vector machines, K-nearest neighbours.
4.2 Unsupervised Learning
- Algorithms like K-means clustering, hierarchical clustering, DBSCAN, principal component analysis (PCA).
4.3 Specialized Areas
- Techniques like natural language processing, image processing, time series analysis, anomaly detection, recommendation systems.
- Deep Learning: Learn to use neural networks, convolutional neural networks (CNN),recurrent neural networks (RNN),Long short-term memory (LSTM), generative adversarial networks (GANs).
- Reinforcement Learning: Basics of reinforcement learning, Q-learning.
6.1 Big Data Technologies
- Understand the basics of big data tools like Hadoop, Spark.
6.2 Cloud Platforms
- Cloud platforms like AWS, Google Cloud, Microsoft Azure.
- Familiarize with cloud-based ML services and data warehousing services.
- Communicate technical results to non-technical stakeholders.
- Understand the basics of industries where you wish to apply data science.
- Effective communication skills and personality development.
- CV preparation.
- Interview Preparation.
- Industry level multi domain expertise.
- Hands on experience on multiple projects.
- Real data driven projects on multiple domains.
- Modelling and Reporting
- Use case preparation to Presentation
- Work on projects that give you real-world challenges to solve.
- Share your work on GitHub, Kaggle or on a blog to showcase your skills.
Major features of our program
Industry oriented learning and training
Live Classes & assessment
Live Classes & assessment
Multi-domain specialization
Personalized Evaluation and Projects
Profile building & Interview Preparation
Training with Essential Tools
Data Scientist Roadmap
Career Opportunities after mastering Data Science
Top Hiring Companies
Bictors is dedicated to exploring the vast world of data science. We believe in the transformative power of data to reshape businesses, drive innovation, and address global challenges. Our mission is to empower individuals with the knowledge and skills needed to excel in the field of data science.
We empower individuals by providing essential knowledge and skills required in the field of data science. Our goal is to nurture future leaders who can navigate the evolving landscape of data science and contribute meaningfully to solving complex problems.
By joining Bictors, you’ll gain insights into the incredible potential of data, learn how it can reshape businesses, and acquire the skills to become a data-driven trailblazer. Our mission is to equip you for a brighter and more impactful tomorrow in the realm of data science.
Bictors sees data as a catalyst for addressing some of the world’s most significant challenges. Through our commitment to data science excellence, we aim to contribute solutions to complex problems and foster innovation on a global scale.
The journey with Bictors is exciting because it opens doors to a world of possibilities in data science. You’ll have the opportunity to shape your future, become a leader in the field, and make a meaningful impact on the world through the lens of data.
Absolutely! Bictors is designed to cater to individuals at all levels, including beginners in data science. Our courses and resources are structured to provide a solid foundation, making the learning experience accessible and rewarding for everyone.