Data Analytics Techniques

IIM Skills in Hyderabad offers a range of data analytics training programs that cover various techniques and methodologies essential for understanding and utilizing data effectively. Their programs typically focus on the following key data analytics techniques:

1. Descriptive Analytics:

   • Data Summarization: Techniques for summarizing data sets, including calculating measures of central tendency (mean, median, mode) and dispersion (standard deviation, variance).

   • Data Visualization: Creating visual representations of data using tools like Excel, Tableau, and Power BI. This includes designing charts, graphs, and dashboards to uncover trends and patterns.

2. Diagnostic Analytics:

   • Exploratory Data Analysis (EDA): Techniques for investigating data sets to identify patterns, correlations, and anomalies. Tools like Python (Pandas, Matplotlib) and R are often used for data cleaning and initial exploration.

   • Statistical Analysis: Applying statistical tests and methods to determine relationships between variables and identify causes of observed outcomes.

3. Predictive Analytics:

   • Regression Analysis: Implementing various regression models (linear and multiple regression) to forecast future values based on historical data. Techniques include using tools like Python (Scikit-learn) and R.

   • Classification Algorithms: Employing machine learning algorithms such as logistic regression, decision trees, and random forests to categorize data into predefined classes. Practical applications often involve using Python or R for model training and evaluation.

   • Time Series Analysis: Analyzing time-ordered data to make forecasts, employing techniques such as ARIMA models and exponential smoothing.

4. Prescriptive Analytics:

   • Optimization Techniques: Using tools like Excel Solver or specialized software to find optimal solutions for various business problems.

   • Simulation: Conducting simulations (e.g., Monte Carlo simulations) to model different scenarios and assess potential outcomes.

5. Advanced Analytics:

   • Machine Learning: Training on advanced machine learning techniques, including supervised and unsupervised learning. This covers algorithms like support vector machines (SVM), clustering methods (e.g., k-means), and deep learning models.

   • Deep Learning: Exploring neural networks and frameworks such as TensorFlow and PyTorch for complex pattern recognition tasks, including image and text data analysis.

6. Big Data Analytics:

   • Big Data Technologies: Introduction to technologies and tools for handling large data sets, such as Hadoop and Apache Spark. This includes practical exercises on data processing and analysis in distributed environments.