Certificates
I am a dedicated learner with certifications from renowned platforms such as Coursera, Simplilearn, DataWorks Ltd, Sparks Foundation, and Pantech Solutions. My expertise spans Machine Learning, Data Science, Data Analytics, Data Visualization, Databases, SQL, and more. These certifications reflect my commitment to staying abreast of industry trends and expanding my knowledge in cutting-edge technologies. I am equipped with a diverse skill set, ready to apply my acquired insights to real-world challenges in the dynamic landscape of data-driven fields.
01
Power Platform Developer
DataWorks Ltd | March 2023 – June 2023
I successfully completed a Power Platform Developer internship at DataWorks Ltd, where I honed my skills in designing impactful reports and dashboards using Power BI. Additionally, I gained expertise in creating customized applications with Power Apps and streamlining processes through flows in Power Automate. My proficiency extends to Microsoft technologies such as SharePoint, MS Dataverse, and OneDrive, enhancing my capabilities in data management and collaboration.
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This internship not only equipped me with technical skills but also provided a valuable opportunity to develop leadership and project management capabilities. I navigated real-world challenges, demonstrating adaptability and effective problem-solving. By combining technical prowess with project management acumen, I am well-prepared to contribute to the seamless integration of technology solutions in dynamic environments.
02
Databases and SQL for Data Science with Python
Coursera | January 10, 2023 to March 5, 2023
In this comprehensive course, I acquired an in-depth understanding of SQL, covering fundamental concepts such as SELECT statements to advanced techniques like JOINs. I mastered essential SQL commands, including SELECT, INSERT, UPDATE, and DELETE, and became proficient in filtering result sets with clauses like WHERE, COUNT, DISTINCT, and LIMIT. Distinguishing between DML and DDL, I learned to create, alter, drop, and load tables.
My knowledge extended to using string patterns, ranges, and employing ORDER and GROUP clauses, along with leveraging built-in database functions. I gained expertise in constructing sub-queries, querying data from multiple tables, and accessing databases as a data scientist using Jupyter notebooks with SQL and Python. The course further covered advanced topics such as Stored Procedures, Views, ACID Transactions, Inner and Outer JOINs through practical labs and projects.
Through hands-on experience, I practiced crafting SQL queries, working with Cloud-based databases, and utilizing data science tools. The culmination of the course involved analyzing multiple real-world datasets, showcasing my acquired skills in a final project.
03
Applied Machine Learning in Python
Coursera | December 9, 2022 to January 5, 2023
Throughout this course, my focus was on applied machine learning, emphasizing practical techniques and methods rather than delving extensively into the underlying statistics. The course commenced by distinguishing machine learning from descriptive statistics and introduced the scikit-learn toolkit through a tutorial. We delved into the challenge of data dimensionality, addressing tasks such as clustering data and evaluating these clusters. The course covered supervised approaches for crafting predictive models, utilizing scikit-learn methods, and addressing process issues related to data generalizability, including concepts like cross-validation and overfitting.
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The concluding segments of the course explored advanced techniques such as building ensembles and delved into the practical limitations of predictive models. By the course's conclusion, I had developed the ability to differentiate between supervised (classification) and unsupervised (clustering) techniques, determine the appropriate method for a given dataset and objective, engineer features to meet specific needs, and proficiently write Python code to execute a comprehensive analysis.
04
Machine Learning with Python
Coursera | August 7, 2022 to November 20, 2022
This course commenced with a gentle introduction to Machine Learning, covering essential topics like supervised versus unsupervised learning, linear and non-linear regression, and simple regression. I delved into classification techniques, exploring algorithms such as K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. The significance of clustering, including types like k-means, hierarchical clustering, and DBSCAN, was also a focal point.
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A notable aspect of the course was its strong emphasis on practical application. I actively engaged in hands-on learning, leveraging Python libraries like SciPy and scikit-learn to implement concepts through various labs. The culmination of the course involved a final project where I showcased my acquired skills by constructing, evaluating, and comparing multiple Machine Learning models employing diverse algorithms.
05
Getting Started With Machine Learning Algorithms
Simplilearn | October 10, 2022 to November 16, 2022
I successfully completed the "Getting Started With Machine Learning Algorithms" course, where I delved into various machine learning algorithms. In the realm of supervised learning, I gained proficiency in algorithms like Linear Regression, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and K Nearest Neighbors (KNN). Additionally, the course covered unsupervised learning algorithms, including K-means Clustering and Principal Component Analysis (PCA). The curriculum also introduced the fascinating field of Reinforcement Learning. This comprehensive learning experience equipped me with a diverse set of tools to approach both supervised and unsupervised machine learning problems, setting a strong foundation for my understanding of machine learning algorithms.
06
Introduction to Data Analytics
Coursera | November 6, 2022 to December 8, 2022
This course provided me with a gentle introduction to Data Analysis, covering the foundational aspects of the role, tools, and skills essential for a Data Analyst. I gained insights into the responsibilities of a data analyst and benefited from advice shared by experienced data professionals for initiating a career in this field. The course effectively clarified distinctions among the roles of Data Analysts, Data Scientists, and Data Engineers.
I immersed myself in the data ecosystem, exploring concepts such as Databases, Data Warehouses, Data Marts, Data Lakes, and Data Pipelines. The journey continued into the realm of Big Data platforms, including Hadoop, Hive, and Spark. By the course's conclusion, I had a solid understanding of the fundamentals of the data analysis process, encompassing data gathering, cleaning, analysis, and presentation through visualizations and dashboard tools.
The culmination of my learning experience occurred in the final project, where I applied my knowledge to tackle real-world data analysis tasks, demonstrating a comprehensive understanding of the course material.
07
Data Science & Business Analytics Internship
The Sparks Foundation | March 01, 2022 – April 30, 2022
I successfully completed the Data Science & Business Analytics internship program at The Sparks Foundation, gaining valuable insights into key areas of the data science workflow. The internship covered a spectrum of essential tasks, including data gathering, preprocessing, and Exploratory Data Analysis (EDA). I honed skills in feature engineering and selection, crucial steps in enhancing model performance. The training and evaluation of models were integral components, providing hands-on experience in optimizing model accuracy. Additionally, I delved into the intricacies of Hyperparameter tuning to fine-tune model performance. The internship culminated in the practical application of acquired knowledge through making predictions, offering a holistic understanding of the end-to-end data science and analytics process.
08
Machine Learning Master class
Pantech solutions ltd | June 07, 2021 to June 11, 2021
I actively participated in the Machine Learning Master Class workshop at Pantech Solutions Ltd, where I gained a comprehensive understanding of various facets of machine learning. The workshop delved into the intricacies of the Machine Learning lifecycle, providing insights into the entire process from data preparation to model deployment. I acquired knowledge about the different types of machine learning, their applications, and the compelling need for integrating machine learning into various domains. The workshop also explored real-world applications of machine learning, highlighting its versatility.
Moreover, the session delved into the challenges and limitations inherent in the field, offering a balanced perspective on the practical considerations involved. This immersive learning experience equipped me with valuable insights, preparing me to navigate the complexities of machine learning in diverse contexts.
