Bhrighu Academy
Data Science Essentials
Certification (IT & Software)
Shortlisted by 2415+ Students
Location: Bengaluru/Bangalore
Duration: 4 MONTH
Hybrid / Online & offline
Average Fees: ₹ 80000
Highlights The Data Science Essentials program is aimed at transitioning students from education to industry by addressing the gap between what graduates have learned in a formal academic setting and the requirements of the Industrial sector. It spans everything from fundamental statistics and data handling to advanced topics like machine learning and data visualization, and engages students through hands-on activities that use tools like Python, R, SQL, and popular Machine Learning libraries. This blend of theory and practice equips them to succeed in today’s data-driven workforce.
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Tools One Will Master
Mastery of essential tools like Python, R, SQL, Jupyter Notebooks, Tableau, and Git/GitHub empowers data science enthusiasts to extract insights, create impactful visualizations, and collaborate effectively in diverse projects. Some of the tools that one will be able to master on completing the training will be:
 Python: Proficiency in Python programming language for data manipulation, analysis, and visualization using libraries like NumPy, Pandas, and Matplotlib. |
 R: Competence in R programming language for statistical analysis, data visualization, and machine learning with packages like dplyr, ggplot2, and caret. |
 SQL: Mastery in SQL (Structured Query Language) for querying and managing relational databases to extract, transform, and analyze data efficiently. |
 Jupyter Notebooks: Skill in using Jupyter Notebooks for interactive data exploration, analysis, and documentation, facilitating reproducible research and collaboration. |
 Tableau: Proficiency in Tableau for creating interactive and insightful data visualizations, dashboards, and reports to communicate findings effectively to stakeholders. |
 Git and GitHub: Competence in version control using Git and collaborative development on GitHub, enabling efficient code management and collaboration in data science projects. |
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