Data Analytics
Duration
6-12 Months
About the Course
The Data Analytics course provides comprehensive training in data collection, analysis, and visualization. Students will learn key concepts such as statistical analysis, data mining, and predictive modeling using tools like Python, SQL, and Tableau. The curriculum covers data preprocessing, machine learning algorithms, and real-world applications. Designed for both beginners and professionals, the course equips learners with the skills needed to turn data into actionable insights. Hands-on projects and case studies ensure practical understanding and application. By the end of the course, participants will be proficient in making data-driven decisions to solve complex problems.
Data Analytics with ML/DL Course: Study Topics and Tools
Study Topics | Tools and Software |
Foundations of Data Analytics: | |
Introduction to Data Analytics | Python (NumPy, Pandas), R (ggplot2, dplyr, caret) |
Data Types and Data Structures | |
Data Cleaning and Preprocessing | Tableau, Power BI, Excel |
Exploratory Data Analysis (EDA) | Matplotlib, Seaborn, Plotly |
Statistical Analysis and Hypothesis Testing | SciPy, StatsModels |
Machine Learning Basics: | |
Introduction to Machine Learning | Scikit-learn, TensorFlow, Keras |
Supervised Learning: Regression and Classification | Linear Regression, Logistic Regression, Decision Trees |
Unsupervised Learning: Clustering and Dimensionality Reduction | K-means, DBSCAN, PCA |
Model Evaluation and Metrics | Cross-validation, ROC Curve, Confusion Matrix |
Advanced Machine Learning: | |
Ensemble Methods and Random Forests | XGBoost, LightGBM, Random Forests |
Support Vector Machines (SVM) | SVMlight, LIBSVM |
Neural Networks Basics | Neural Network Architectures, Activation Functions |
Deep Learning Fundamentals | TensorFlow, PyTorch, CUDA |
Convolutional Neural Networks (CNN) | Image Processing, Computer Vision |
Recurrent Neural Networks (RNN) | Natural Language Processing (NLP), Sequence Prediction |
Advanced Topics in Data Analytics: | |
Natural Language Processing (NLP) | NLTK, SpaCy, Gensim, Transformers |
Time Series Analysis and Forecasting | ARIMA, Prophet, LSTM |
Anomaly Detection and Outlier Analysis | Isolation Forest, One-Class SVM, Autoencoders |
Big Data Technologies and Tools | Hadoop, Spark, Kafka, Elasticsearch |
Tools and Environments: | |
Integrated Development Environments (IDEs) | Jupyter Notebook, RStudio, PyCharm |
Version Control Systems | Git, GitHub, GitLab |
Cloud Platforms | AWS (Amazon Web Services), Google Cloud Platform, Microsoft Azure |
Database Management Systems (DBMS) | MySQL, PostgreSQL, SQLite, MongoDB |
Data Warehousing and ETL Tools | Talend, Informatica, Apache NiFi |
Visualization Tools | Tableau, Power BI, D3.js, Plotly |
Syllabus will Change*