Features
- Lectures - 58
- Duration - 26 Weeks
- Case Studies and Assessment - 15+
- Delivery Mode - Online/Offline
- Batches - Weekdays/Weekend
- Capstone Projects - 10 projects( Choose anyone)
- Zero Cost EMI Options Available from RBI Approved NBFCs
Data Science is a cross-disciplinary blend of tools and technologies that work conjointly to understand business, clients, and patterns and resolve inquisitions that we have not yet perceived from the data stored in data warehouses and all possible web applications. This 440-hour Nasscom data science certification program is the perfect blend of Statistics, Programming, Machine learning, Deep Learning, Artificial Intelligence, and Data Visualization designed to give you a holistic view of Data Science.
Program Overview
- The most comprehensive curriculum with training material designed by NASSCOM, along with its 31 SIG(Special Interest Group) members such as Goldman Sachs, IBM, Ins Analytics, Infosys BPO, Insights of Data, JP Morgan, Karvy Analytics, Knod Global, KPMG, Wipro, WNS, Wells Fargo, Amazon, Capgemini, Concentrix, CITI, Cyient Insights, Accenture, EXL, First America, Fractal Analytics, GENPACT, Google, ADP Deloitte, HCL, HDFC, IBM, ISC2, NIIT University, PwC, Symantec, TCS to name a few,that will prepare you for future externalities in the data analytics industry and fulfill the gap of academics and industry requirements.
This officially NASSCOM-certified 440 hours Data science program covers R programming, Python training, Statistics, Machine Learning Algorithms, Deep Learning, Artificial Intelligence, Time-Series forecasting, SQL, and Tableau.
Nasscom 440 Hours Data Science Certification Program Structure
- 50 hours Pre-Learning: Before you come in, get ready for the program. You will get a series of online recorded tutorials to understand the structure of Data Science.
- 137 Hours Program: Here, you will get Hands-on Experience in advanced Excel, SQL, R Programming, Python Programming, Statistics, Machine Learning, and Tableau.
- 253 hours Post Program: Learning does not stop here. After completing the Program, you will work on Projects and Assignments. Doubt clearing session is also provided. You will be working on any one capstone project from the list of a few projects of your choice.
- 96 Hours of Electives: Grab the opportunity to add advanced knowledge on data science, artificial intelligence, and business intelligence tools to the existing pool of knowledge by opting for the electives. Here you will be working on advanced concepts of statistics, machine learning algorithms, SQL business intelligence tools like Tableau and Power BI, and object detection skills and speech recognition.
Price :
- Elective 1: Advanced Data Science and Neural Network, INR 24999+GST, $407
- Elective 2: Advanced Business Intelligence, INR 11,999+GST, $220
- Elective 3: Advanced Artificial Intelligence with Computer Vision & Natural Language Processing, , INR 11,999+GST, $220
Eligibility:
- Work Exp– Working professionals in IT / Analytics / Statistics / Machine Learning.
- Education – Fresh Graduates from Engineering/ Mathematics / IT backgrounds.
Sample Certificate
Nasscom Certificate –
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Advanced Excel
Microsoft Excel Overview, Formatting excel, Shortcuts, Basic Formulas, Sorting data, Filtering data, Column chart, Pie chart, Pivot tables, VLOOKUP, Match Function, Lookup Function, VBA
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SQL
BASIC OF DATABASE, TYPES OF DATA BASE, DATA TYPES, SQL OPERATORS, EXPRESSION (BOOLEAN, DATE, NUMERIC), CREATE, INSERT, DROP, TRUNCATE, DELETE, ALTER, UPDATE, SELECT, RANGE OPERATER, IN, WILDCARD, LIKE CLAUSE, CONSTRAINT, AGGREGATION FUNCTION, GROUP BY, ORDER BY, HAVING, JOINS, CASE, COMPLEX QUERIES
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R-Programming
What is R-Programming?, Variables and Data Type in R, Logical Operators, Vectors, List, Matrix, Data Frame, Flow Control, Functions in R, Data Manipulation in R- dplyr, Data Manipulation in R- tidyr, Data Visualization In R.
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Python Programming
Introduction to Python, What and Why Python, Variables,? Datatypes and operation, Operators, Block Structure, Data Structures, Functions, Modules, Class, Numpy, Pandas, Matplotlib, Seaborn
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Statistics
Introduction to Statistics, Descriptive Statistics, Population and Sample, Types of Data, Percentile, Quartile, IQR, Correlation and Covariance, Measure of Central Tendency, asymmetry and variability , Skewness, Kurtosis, Central Limit Theorem, Confidence Interval, Hypothesis Testing, p-value, T-test, Z-test, F-test
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Machine Learning and Deep Learning
Introduction to Machine Learning, Types of Machine Learning, Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, K-Nearest Neighbor, Support Vector Machine, Random Forest, PCA, K-Means, Introduction to Neural Network, Foreword Propagation, Activation Function, Optimizers, Padding, Pooling, Convolution, Check Points, Neural Network Implementation, Time Series Analysis
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Artificial Intelligence [Computer Vision, Natural Language Processing, Reinforcement Learning]
✔️ Computer Vision
Introduction to Image Processing, Feature Detection, OpenCV, Convolution, Padding, Pooling & its Mechanisms, Forward Propagation & Backward Propagation for CNN CNN Architectures AlexNet, VGGNet, InseptionNet, ResNet, Transfer Learning
✔️ Natural Language Processing
Introduction to text mining, text processing using python, Introduction to NLTK, Tokenization, Stemming, Bag of words, Sentiment Analysis, Name- Entity Recognition, Text Segmentation, Text Mining, Text Classification
✔️ Reinforcement Learning
RL Framework, Component of RL Framework, Examples of Systems, Types of RL Systems, Q-Learning -
Tableau
TABLEAU PRODUCTS, INSTALLATION OF TABLEAU DESKTOP/PUBLIC, CONNECTING TO DATABASES, REPLACING DATASOURCE, TABLEAU CANVAS INTERFACE, DATA TYPES, DRILL DOWN, HIREARCHIES, MEASURES, DIMESIONS, SORTING, GROUPING, PARAMETER, SETS, COMBINE, DATA BLENDING, FILTER, MARKS CARD, DUAL AXIS, CALCULATED FIELD, VISUALZATION, CANVAS FORMATING, DASHBOARD CREATION
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Elective 1: Advanced Machine Learning and Neural Networks
- ✔️ Probability
- ✔️ Probability distribution -> Discrete Probability Distribution (Probability Mass Function, Binomial Distribution, Poisson Distribution, Discrete Uniform Distribution) Continuous Probability Distribution (Probability Density Function, Normal Distribution, Continuous Uniform, Exponential Distribution)
- ✔️ Hypothesis Testing (Z Distribution, Student’s T distribution [ One Sample T-test, Two Sample T-test], Chi Square [Chi-Square goodness of fit, Chi square test of independence])
- ✔️ Bayes Theorem
- ✔️ Correlation [Pearson Corelation, Linear Corelation]
- ✔️ Cross Validation [ K-fold cross-validation, Hold-out cross-validation, Stratified k-fold cross-validation]
- ✔️ Hyperparameter Tuning
- ✔️ Ensemble Learning [Lasso Regression, Ridge Regression, Adaboost, Adagrad]
- ✔️ Time Series Analysis (Introduction to TSA, Component of TSA, Methods to check stationarity [ ADF, KPSS] , AR, Moving Average [SMA, CMA, EMA], ACF, PACF, ARMA, ARIMA, LSTM, GRU)
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Deep Learning
- ✔️ Optimizers [SGD, RMSProp, Adam, Adadelta, Adamax, Adagrad, Adam]
- ✔️ Activation Function [Linear , Sigmoid, Tanh, ReLu, Leaky ReLu, ELU, Softmax, Swish]
- ✔️ Loss -> Classification (BinaryCrossentropy class, CategoricalCrossentropy class, SparseCategoricalCrossentropy class, Poisson class, binary_crossentropy function, categorical_crossentropy function, sparse_categorical_crossentropy function) Regression (MeanSquaredError class, MeanAbsoluteError class, MeanAbsolutePercentageError class, MeanSquaredLogarithmicError class, CosineSimilarity class)
- ✔️ Weight Initialization (Xavier Weight Initialization, Normalized Xavier Weight Initialization, He weight Initialization)
- ✔️ Bias Initialization
- ✔️ Callbacks (ReduceLROnPlateau, ModelCheckpoint, Earlystopping, TensorBoard)
- ✔️ Keras Tuner
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Elective 3: Advanced Business Intelligence
- ✔️ SQL (Trigger, Stored Procedures, Common Table Expression, Index, Except, Exists, Grouping set, Pivot, Rollup, Cube, Constraints, Partition)
- ✔️ Tableau (Pareto Analysis, Table calculation, Multiple data source blending, Advanced charting techniques, Tableau prep tool)
- ✔️ PowerBI (Multiple data source blending, Basic of SSIS for ETL)
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Elective 4: Advanced Artificial Intelligence with Computer Vision and Natural Language Processing
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Program Benefits
- ✔️ Cutting Edge Curriculum: Hand crafted Course content made by Experts from various Industries. Learn through Practical case studies and multiple projects.
- ✔️ Build Solid Foundation: 137 hours focused course on Data Science.
- ✔️ On the Go Learning: Online accessible E-learning Material, recorded lectures, case studies and Research Paper through our system.
- ✔️ On the Go Learning: Online accessible E-learning Material, recorded lectures, case studies and Research Paper through our system..
- ✔️ Industry Mentorship: Get 1 to 1 guidance from Industry experts and start your career in Data Science.
- ✔️ Earn a Government of India approved & globally recognized certificate by NASSCOM IT- ITeS SSC by clearing NASSCOM assessment examination.
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Skills you will possess post program
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Capstone Projects
- The most effective way to learn Data Science is to learn practically. Once the program gets finished, candidates will be provided with a few Projects based on Machine Learning. You are advised to choose any 1 project according to your domain and your interest. Some examples of capstone projects:
- ✔️ Prediction of Future security prices
- ✔️ Credit card Risk Analytics
- ✔️ Sales Prediction for Big Mart
- ✔️ Food Demand Forecasting
- ✔️ House Price Prediction
- ✔️ Black Friday Sales Prediction
- ✔️ Social Media Analysis(Sentiment Analysis)
- ✔️ Image Recognition Using Computer Vision
- ✔️ Recommendation System
- ✔️ Image Classification Using Computer Vision
- ✔️ Fake News Prediction
- ✔️ Fake Job Analysis
- ✔️ Consumer Review
