• • Data collection methods and sources
• • Descriptive statistics
• • Univariate and bivariate analysis
• • Data visualization techniques for exploring and understanding data
• • Data cleaning and handling missing values
• • Outlier detection and treatment
• • Data transformation and feature engineering

• • Fundamentals of probability theory
• • Probability distributions
• • Sampling techniques and sampling distributions
• • Confidence Intervals and their interpretation
• • Hypothesis testing: null and alternative hypotheses, p-values, type I, type II errors, and A/B testing
• • One-sample t-test and independent samples t-test
• • Paired samples t-test
• • Chi-square test for independence
• • Analysis of Variance (ANOVA)
• • Correlation analysis
• • Regression Analysis
• • Time Series Analysis
• • Forecasting techniques

• • Linear Algebra
• • Hyperplane
• • Calculus
• • Optimization
• • Gradient descent
• • Graph and Information Theory
• • Discrete Mathematics

• • Overview of machine learning concepts, and Applications.
• • Bias-variance tradeoff, model complexity, underfitting, and overfitting.
• • Feature selection and extraction techniques.
• • Scaling, normalization, standardization, and encoding of categorical variables.
• • Model evaluation: Metrics, Cross-validation, and train-test split.

• • Linear regression: Simple linear regression, multiple linear regression.
• • Regularization techniques: Ridge, Lasso, Elastic Net.
• • Polynomial regression and feature engineering.
• • Non-linear regression: Support Vector Regression (SVR), Decision Tree Regression.
• • Evaluation metrics for regression: Mean Squared Error (MSE), R-squared, Adjusted R-squared, etc...
• • Hyperparameter tuning: Grid search, Random search.

• • Logistic regression: Binary and multiclass classification.
• • K-nearest neighbors (KNN): Instance-based classification algorithm.
• • Naive Bayes classifier: Gaussian, Multinomial, Bernoulli.
• • Decision trees: Construction, pruning.
• • Hyperparameter tuning: Grid search, Random search.
• • Evaluation metrics: Accuracy, Precision, Recall, F1-score, ROC curve, AUC.

• • SVM for binary and multiclass classification.
• • Kernel methods: Linear, polynomial, and Gaussian RBF kernels
• • Bagging: Bootstrap aggregating.
• • Boosting: AdaBoost, Gradient Boosting.
• • Stacking: Model stacking and blending.
• • Hyperparameter tuning: Grid search, Random search.

• • K-means clustering: Elbow method, silhouette score.
• • Hierarchical clustering: Agglomerative, divisive clustering, and dendrograms.
• • Density-based clustering: DBSCAN.
• • Dimensionality reduction: Principal Component Analysis (PCA), t-SNE
• • Evaluation metrics for clustering: Silhouette score, Dunn index.
• • Anomaly, Outlier, Novelty Detection.
• • Recommender Systems

• • Time Series Data Preprocessing: Stationarity, differencing.
• • Autoregressive Integrated Moving Average (ARIMA).
• • Seasonal ARIMA (SARIMA) and seasonal decomposition.
• • Forecasting Techniques: Exponential smoothing, Prophet

• • Neural Networks.
• • Perceptron, MLP architecture, activation functions, weight initialization, Hidden Layers, Callbacks, Forward and Back Propagation, and optimization techniques.
• • Hyperparameter tuning.
• • Convolutional Neural Networks (CNN)
• • CNN Architecture - Alex, VGG, ResNet, Inception, EfficientNet, MobileNet
• • Object Segmentation, Localisation, and Detection
• • Recurrent Neural Networks (RNN)
• • Sequence modeling, language generation, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU).
• • Generative Adversarial Networks (GAN).
• • Image generation, style transfer, generator and discriminator networks, and loss functions.

• • Text Preprocessing: Tokenization, stemming, Lemmatization, stop-word removal.
• • Vector space modeling, Cosine Similarity, Euclidean Distance.
• • POS tagging, Dependency parsing.
• • Word Embeddings: Word2Vec, Glove.
• • Recurrent Neural Networks (RNN) for NLP tasks.
• • Sentiment Analysis, Named Entity Recognition, Text Classification.

• • Excel basics: navigating the interface, data entry, formulas, and functions.
• • Data Cleaning and Preparation in Excel.
• • Advanced Excel Formulas and Functions.
• • Data Visualization with Excel.
• • VBA (Visual Basic for Applications).

Note: For the complete syllabus kindly refer to the Excel curriculum.

• • Setting up Excel for data analysis: installing necessary add-ins and tools.
• • Using Excel's statistical functions.
• • Power Query for data cleaning and transformation
• • Power Pivot for advanced data modeling and analysis.
• • Statistical Analysis in Excel
• • Using Excel's data analysis tools: Solver, Goal Seek, and Scenario Manager.

• • Data Manipulation
• • Data Filtering and Transformation
• • Summarizing Data and Essential Functions
• • Transactions and Concurrency
• • Indexing for High Performance
• • Securing Databases

Note: For the complete syllabus kindly refer to the SQL or MongoDB (NoSQL) curriculum

• • Statistical functions.
• • Analytical functions.
• • Window functions for advanced data analysis.
• • Moving averages and cumulative sums.
• • Partitioning data for analysis.
• • Exporting query results to external formats.

• • Overview of Power BI/Tableau and their capabilities
• • Cleaning and transforming data using Power Query or Tableau Prep.
• • Understanding data modeling concepts
• • Advanced calculations using DAX or Tableau calculations.
• • Utilizing functions for time intelligence, statistical analysis, and ranking.
• • Building interactive dashboards with multiple visualizations.
• • Aggregating data and calculating key performance indicators (KPIs).
• • Storytelling techniques to present data insights effectively

Note: For the complete syllabus kindly refer to the Power BI or Tableau curriculum

• • Python Basic Constructs.
• • Python Control Flow and Functions.
• • Python Data Structures.
• • Exception, File Handling, and Modules.
• • Object-Oriented Programming.
• • Advanced Python Concepts.

Note: For the complete syllabus kindly refer to the Python curriculum

• • NumPy for efficient numerical computations.
• • Arrays and multidimensional arrays: creation, indexing, slicing, and manipulation.
• • Mathematical operations, linear algebra, and statistical functions in NumPy.

• • Pandas Library for data manipulation and analysis.
• • Working with Series and DataFrames: indexing, selection, filtering, and grouping.
• • Data cleaning and preprocessing techniques, and handling missing data.
• • Data transformation: merging, reshaping, and pivoting datasets.

• • Exploratory data analysis techniques to uncover patterns and relationships.
• • Matplotlib and Seaborn for data visualization.
• • Plotting techniques: line, scatter, bar, histograms, and box plots.
• • Customizing plots, adding labels, titles, legends, and annotations
• • Advanced interactive data visualization using Plotly and Bokeh
• • Creating interactive plots, dashboards, and widgets.
• • Geospatial data visualization and mapping

• • Exploratory data analysis techniques for gaining insights from data.
• • Statistical summaries, correlation analysis, and data profiling.
• • Visualizing relationships, distributions, and patterns in data.
• • Descriptive statistics, hypothesis testing, and confidence intervals.
• • Regression, ANOVA, and model diagnostics.
• • Time Series Analysis and Forecasting Techniques.

• • Introduction to big data concepts and Apache Spark framework.
• • Distributed computing with PySpark: RDD, DataFrame, and SQL.
• • Handling large-scale datasets and performing data analysis using PySpark.

• • Supervised Learning: Regression, classification, model evaluation, and validation techniques with Scikit-learn.
• • Unsupervised Learning: Clustering, dimensionality reduction, and anomaly detection with Scikit-learn.
• • Ensemble Methods: Random Forest, Gradient Boosting, and model stacking with Scikit-learn.
• • Deep Learning: Neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN) with TensorFlow.
• • Text Preprocessing: Tokenization, stemming, stop-word removal, and feature extraction with NLTK.

• • Introduction to version control systems and their importance in collaborative software development.
• • Learning the basics of Git for tracking changes in code and project files.
• • Leveraging GitHub for collaboration, sharing code repositories, and managing projects.

• • Working on a comprehensive data science and Al project from start to finish using Python and relevant libraries.
• • Applying machine learning and Al techniques to solve real-world problems.
• • Presenting project findings, insights, and recommendations.

• • We provide a soft copy of the topics (Reading, Videos, and Articles) along with assignments
• • After the training, they are implementing real time project with a duration of 25 hrs, this will get them the confidence about there learning’s from this courses
• • Along with technical teachings, we also guide to build Professional resume along with Mock Interview sessions and organize real time interviews to get their job.

• • By undergoing this training the candidates will be a ready resource who can kick start there carrier in Embedded Technology.

• •As a fresher you can apply for associate software engineer or Testing Engineer (Verification & Validation) positions?

• Continental, KPIT, BOACH, TATA Elxsi and start-ups companies.

• 3.8 LPA - 7 LPA