By the end of this course, you'll be well-equipped to launch a rewarding career in data science.
DurationThe Data Science short course provides participants with a comprehensive introduction to the fundamental concepts and practical techniques used in data science. Beginning with basic statistical concepts and data manipulation in Python, participants will progress to exploratory data analysis, data visualization, and feature engineering.
The course covers essential machine learning algorithms, including regression, classification, and clustering techniques, as well as model evaluation and validation methods. Throughout the course, emphasis is placed on hands-on learning through practical exercises and real-world case studies, enabling participants to gain practical experience in applying data science techniques to solve business problems.
By the end of the course, participants will have acquired the skills and knowledge necessary to pursue further study or careers in the field of data science.
Course Duration: 6 Months Certification Course (4 Months Live Sessions and 2 Months Internship)
Tools Covered: Python, Power BI, Excel, NumPy, Pandas, Scipy, Seaborn, MySQL, & more
Smartzett Academy recognizes the importance of well-rounded development. We offer a variety of short-term courses that equip students and professionals with in-demand skills.
If your dream is to study and work abroad, or you're looking to upskill for a global job market — Smartzett Academy provides the right tools to achieve your goals.
We achieve this by providing comprehensive guidance and support for those seeking to build successful international careers. This includes offering a diverse range of educational programs, personalized support services, and access to a network of over 700 universities worldwide. IATA, AI-powered courses such as Data Science, Cyber Security, Overseas education etc.
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This hands-on training will help you in setting the way for your desired Microsoft Certification whether you are a beginner or an experienced professional.Â
It includes discussions and learning about the history of AI, state-space problem representations, understand machine learning algorithms, intelligent agents, logical agents, game playing, uninformed and heuristic search and constraint satis
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