Data Analytics – Self Paced

Master the world of data analytics with this comprehensive course designed for aspiring data scientists, analysts, and machine learning enthusiasts. This program covers everything from statistical analysis, Python programming, and machine learning, to big data technologies, NLP, time series forecasting, and production deployment.

Start with a strong foundation in statistics and probability, including hypothesis testing, ANOVA, regression analysis, Bayesian statistics, time series analysis, and experimental design. Learn to interpret data, test hypotheses, and make informed decisions using statistical inference.

Advance your skills in Python for Data Science, exploring libraries like NumPy, Pandas, Matplotlib, Seaborn, and tools for data cleaning, pre-processing, normalization, feature engineering, and handling missing data or outliers. Gain the ability to transform raw data into actionable insights efficiently.

Dive deep into machine learning with supervised, unsupervised, and semi-supervised algorithms. Explore advanced techniques such as deep learning (CNNs, RNNs, LSTMs), ensemble methods, reinforcement learning, and model selection. Learn to build robust predictive models and extract meaningful insights from complex datasets.

Get hands-on experience with big data technologies, including Hadoop ecosystem (HDFS, MapReduce, Hive), Spark and Spark MLlib, distributed databases like Cassandra, MongoDB, HBase, NoSQL systems, and real-time stream processing. Learn to analyze and manage large-scale datasets efficiently.

Master data visualization and exploration using libraries and tools like ggplot, Bokeh, Plotly, D3.js, Tableau, and techniques for geospatial, network, and interactive data visualization. Conduct exploratory data analysis to uncover trends, patterns, and anomalies.

Learn natural language processing (NLP) techniques including text preprocessing, feature extraction, sentiment analysis, topic modeling, named entity recognition, and deep learning for NLP (RNNs, LSTMs, Transformers).

Advance your expertise in time series analysis and forecasting, using ARIMA, SARIMA, Prophet, and LSTM models. Implement anomaly detection, event forecasting, and fine-tune predictive models for business-critical applications.

Finally, learn to deploy and productionize data science projects using Docker, Kubernetes, and cloud platforms like AWS, Azure, and Google Cloud. Understand scalable data pipelines, monitoring, logging, and model deployment for real-world analytics solutions.

By the end of this course, you will be proficient in data analytics, machine learning, big data, NLP, time series forecasting, and production deployment, equipping you for high-demand roles in data science and analytics.

📖 Course Curriculum (Highlights)

1. Statistics and Probability: Hypothesis testing, ANOVA, regression, Bayesian statistics

2. Python for Data Science: Advanced Python, NumPy, Pandas, Matplotlib, Seaborn, Data Cleaning & Pre-processing

3. Machine Learning: Supervised, unsupervised, semi-supervised, deep learning, ensemble methods

4. Big Data Technologies: Hadoop, Spark, distributed and NoSQL databases, real-time analytics

5. Data Visualization & Exploration: ggplot, Bokeh, Plotly, D3.js, Tableau, geospatial and network visualization

6. Natural Language Processing: Text preprocessing, sentiment analysis, topic modeling, deep learning

7. Time Series Analysis: ARIMA, SARIMA, Prophet, LSTM, anomaly detection, forecasting

8. Deployment & Productionizing: Docker, Kubernetes, AWS/Azure/Google Cloud, scalable pipelines, monitoring

🌟 Why Enroll in This Course?

✔ Master advanced data analytics techniques for real-world applications
✔ Gain hands-on experience with Python, machine learning, and big data tools
✔ Learn NLP, time series forecasting, and interactive visualizations
✔ Understand deployment and productionizing models in cloud environments
✔ Prepare for high-demand roles like Data Scientist, Machine Learning Engineer, and Analytics Specialist