In this Class You will Gain an Real World Experience like Data Science, Tools, Data Visualization, Programming Language, Some Basics and Advance Concepts of Data Analysis with Examples.

Data Collection: Gathering data from various sources such as databases, sensors, and online platforms.
Data Processing: Cleaning, transforming, and organizing raw data to ensure its quality and usability.
Data Analysis: Using statistical methods, exploratory data analysis (EDA), and visualization to understand trends, patterns, and relationships in the data.
Machine Learning: Applying algorithms that allow computers to learn from data and make predictions or decisions without being explicitly programmed.
Data Visualization: Presenting the results through charts, graphs, dashboards, or reports to communicate findings effectively to stakeholders.
Big Data: Handling and analyzing large datasets that traditional tools and methods cannot manage.
And Some Real Applications Like Healthcare Industry, Finance, Aerospace, Marketing and Transportation.

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In a Data Science class, you'll learn how to work with data to extract valuable insights and solve real-world problems. The course will cover key concepts like data collection, cleaning, and processing, followed by techniques for analyzing and visualizing data. You'll also explore machine learning algorithms, which help in making predictions from data, and tools for working with big datasets. By the end, you'll be able to apply data science methods to industries like healthcare, finance, or marketing and make data-driven decisions.

About the Trainer

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T. Sai Krishna Manas

Master of Engineering - Master of Technology (M.E./M.Tech.)

3 Years of Experience

I am a passionate Data Scientist with an MTech in Data Science and hands-on experience in building and optimizing machine learning and AI models to solve complex problems. My work spans across predictive modeling, deep learning architectures, and Big Data solutions. I am particularly skilled at leveraging data-driven insights to support decision-making and improve business outcomes. During my internships at Innomatics Research Labs and Skill Upward, I worked on exciting projects, including developing models for Alzheimer’s disease prediction using CNN architectures and implementing Big Data solutions such as creating data warehouses with Apache Hive. I enjoy working with cutting-edge technologies like transfer learning and architectures such as ShuffleNet, EfficientNet-B0, and NASNet-Mobile to optimize model performance and execution speed.
Technical Skills:
Machine Learning: Classification(Logistic Regression, SVM, Decision Tree, Random Forest, XGBoost, ANN, etc), Regression (Linear and Polynomial Regression), Optimization, Clustering (KMeans, DBScan, Hierarchical Clustering), Computer Vision, Time Series Analysis, Natural Language Processing.
Deep Learning: CNN, RNN, ANN, Auto Encoders, GAN, GRU, LSTM, Image Segmentation, Object Detection, Automatic Image Captioning etc.
Programming: Python, MATLAB, R
Databases: MySQL, NoSQL
Data Analysis: Pandas, NumPy, Scikit-learn, Exploratory Data Analysis and Data Visualization
Big Data: Apache Hive, HiveQL, Hadoop, Apache Spark, Apache Kafka,
Data Modelling: TensorFlow, Keras, sklearn, pytorch, opencv etc.
Visualization: Power BI, Tableau, Pandas
Beyond technical expertise, I have developed management skills through project coordination, team collaboration, and data-driven decision-making. I thrive in environments where I can work with cross-functional teams and deliver impactful results.
I am always eager to learn, adapt, and contribute to new and innovative projects, particularly those at the intersection of AI, machine learning, and Big Data