Training & Placement Enablement Across

  • Artificial Intelligence Training
  • Custom Application Development (java, angular etc)
  • Emerging Technology (AI, ML, IoT, Bigdata, NoSQLDB etc)
  • Software Testing (Functional, Automation, Performance and Security Testing)

HDSC is paving the way of Human Resource across technology consulting, business consulting and engineering service etc. across industry sector.

HDSC provides highly qualified and technical software professional on a permanent and long term contractual basis to companies with such needs, meeting their immediate hiring needs and in acheiving long term business goals & objectives.

Training IT professional to develop cutting-edge skill and maintian knowledge is critical for digital transformation Training Model:Class room training, Online platform training, fastrack training, Corporate training.

ERP SKILL
  • SAP Business One
  • SAP ECC & SAP S/4HANA
  • SAP SuccessFactor
  • SAP Ariba
  • Pega
  • Oracle
  • SFDC
EMERGING TECHNOLOGY
  • Machine Learning
  • Artificial Intelligence
  • Data Science
  • IoT
  • Cyber Security
  • Cloud Technology
  • Big Data
  • NoSql Database
CUSTOM APPLICATION DEVELOPMENT
  • Java and .Net
  • Python
  • Analytics
  • IT Architech
  • Project Management
  • Agile Coaching
SOFTWARE TESTING TRAINING
  • Functional Testing
  • Automation Testing
  • Performance Testing
  • Security Testing
  • Mobile Device Testing
  • Cloud Application Testing
  • Big Data Testing
SI.No Module Description
1 Predictive Analysis Statistical Foundations, Probability, Inferential Statistics, Regression.
2 R, Python for AI-ML Environment, Markdown, Object introduction.. Variables, Strings, Functions, Loops, Conditions, Vectors, Matrix, Lists etc. Work with data in including reading and writing files, loading, working and saving data. Perform data Preprocessing, manipulation and data visualization using popular libraries.
3 Regression and Classification Detail of Regression and Classification Problems Introduction to linear regression and logistic regression, predictive modeling, simple linear regression vs multiple linear regression, concepts, formulas, assumptions and residuals in Linear Regression, confusion matrix, ROC, cost function, evaluating coefficients and building a simple linear model.
4 Machine Learning Introduction to Machine Learning, types of ML ( Supervised and Unsupervised ). Example and Applications of Supervised and unsupervised Learning.
5 Unsupervised Learning & Supervised Learning Concept of Distance and related math background, K-Means Clustering and hierarchical clustering, Association Rules, Apriori,PCA Supervised Learning-Real-Life Scenario,learning flow, KNN, SVM,Decision tree, Ensemble techniques-Bagging, stacking, boosting
6 Time Series Learning Introduction to time series, stationary, ACF and PACF, ARIMA, ARIMAX. Solving a business case un class with an objective to revise the model and data science pipeline.
7 Foundations of Text Mining and Search Introduction to the jagron:text mining, information retrevial, unstructured to structured data preparation, modeling approaches: TFIDF , Text classification, rule based classification, supervised learning, sentiment analysis
8 AI Decision Science Linear Programming: transportation, assignment, integer problems. Genetic Algorithm. ANN-Motivation for Neural Networks and its Applicaton; Perceptron and single layer. Neural network and hand calculation; learning in a multi layered neural Net: Back propagation and conjugant gradient techniques; deep learning-autoencoders, features generation, dropout and batch normalization. Motivation for convolution neural networks. Background of image processing and application. word embedding (skip-gram, glove), RNN (Word2vec), drawbacks of RNN and motivation for LSTM (entity extraction Visualization).
9 The Arts and Science of Storytelling with Data Visualization Storytelling-A great art and science. Primary ingredients of data visualization. case highlighting the transition from a simple chart to a powerful visualization. R-ggplot. python-matplotlib, plotly, seaborn, tableau.
SI.No Module Description
1 Introduction to basic of R, Python and Hadoop 1. Environment, Markdown, Object introduction...Variables, strings, functions,Loops, conditions, vectors, matrix, lists etc. 2. Work with data in including reading and writing files, loading, working and saving data 3. perform data analytics using popular libraries.
2 Foundations of Probability and Statistics Understand essential statistical concepts including measures of central tendency, normal distribution; central limit theorem. Inferential statistics: Confidence intervals, Hypothesis Testing, dispersion, correlation and regression
3 Essential Engineering Skills in Big Data Analytics Using R, Python, Hadoop and Spark Ecosystems Advanced data manipulation function, ML Function, R, Python, Hadoop basic function, role of YARN and spark SQL Data Frames.
4 Statistics and Probablity in Decision Modeling: Linear Regression Machine Learning algoritms, Big picture of the application. (Understand and use linear and non-linear regression models: Probabilistic interpretation )
5 Statistics and Probablity in Decision Modeling: Logistics Regression Why Linear Rgression fails and logit function, constructing logistic regression, diagnostic. MLE, gradients.Inductive Learning:Bias Versus Variance, learning Curves, ROC
6 Statistics and Probablity in Decision Modeling: Native Bayes Classifier, Regularization L1,L2 and Elasticnet Regularization, Native Bayes classifier and PCA
7 Statistics and Probablity in Decision Modeling: Time Series Introduction to time series, stationary, ACF and PACF, ARIMA, ARIMAX, Solving a business case in class with an objective to revise the models and data science pipeline visualizations.
8 The Art and Science of Storytelling with Data Visualization Storytelling a great art and science.Primary ingredients of data Visualization. Case highlighting the transition from a simple chart to a poerful visualization. R-ggploy. python - matplotib, ploty, seaborn, tableau