1. Digiperform
Digiperform is considered one of India’s largest coaching firms with a give attention to digital abilities. A crew of over 50 digital advertising specialists is placing collectively and updating our programs every day. We’re going to consider 450 skilled professionals from corporations throughout Asia who’ve despatched us their opinions and suggestions.
Our curriculum is rigorously crafted to cowl the important abilities wanted for positions in each small and huge Knowledge Science businesses, in addition to for firms with in-house Knowledge Science groups. At Digiperform, we emphasize a hands-on studying method by integrating sensible workouts, analysis, and assignments to offer a complete and sensible studying expertise.
Why Select Digiperform On-line Knowledge Science Course?
In India, we provide the most effective coaching program in knowledge science, which goals to arrange you on your profession as an information scientist. You’ll work on 75+ initiatives and assignments that cowl statistics, superior Excel, SQL, Python libraries, Tableau, Superior Machine Studying in addition to Deep Studying. These duties are designed to simulate real-world knowledge issues in quite a lot of sectors, reminiscent of well being care, manufacturing, gross sales, media, advertising, and training. You’ll study greater than 30 totally different roles this coaching will put together you for.
Knowledge Science On-line Course: Benefits
There are plenty of benefits to enrolling in on-line knowledge science programs. To begin with, you may study proper right here at residence, saving money and time on journey. These programs are inclined to have versatile schedules, which let you examine when it fits you greatest. They’ll offer you plenty of instruments like movies and interactives to assist together with your research. To be able to ask questions or get assist, you might also have the ability to speak with fellow college students and specialists on the Web.
Knowledge Science Course Syllabus
Module 1: Introduction to Knowledge Science | Introduction to the Business & Buzzwords Industrial utility of knowledge science Introduction to totally different Knowledge Science Strategies Necessary Software program & Instruments Profession paths & progress in knowledge science |
Module 2: Introduction to Excel | Introduction to Excel- Interface, Sorting & Filtering, Excel Reporting- Fundamental & Conditional Formatting Layouts, Printing and Securing Information |
Module 3: Introduction to Stats | Introduction to Statistics & It’s Purposes Intro: Inferential vs. descriptive statistics |
Module 4: Descriptive Stats Utilizing Excel Datasets | Categorical Variables Visualization Utilizing Excel Charts- FDT, Pie Charts, Bar Charts & Pareto Numerical Variables Visualization of Frequency & Absolute Frequency- Utilizing Histogram, Cross Desk & Scatter Plot Measure of Unfold ( Imply, Mode , Median) Measure of Variance( Skewness, SD, Variance, Vary, Coef. Of Variance, Bivariate Evaluation, Covariance & Correlation) |
Module 5: Inferential Stats Utilizing Excel Datasets | Introduction to Chance Permutation & Mixtures Normal Regular distribution Regular vs. Normal Regular distribution Confidence Intervals & Z-Rating Speculation Testing & It’s Varieties |
Module 6: Database Design & MySQL | Relational Database idea & Introduction to SQL Database Creation within the MySQL Workbench Case Statements, Saved Routines and Cursors Ø Question Optimisation and Greatest Practices Ø Drawback-Fixing Utilizing SQL |
Module 7: Knowledge Visualization Utilizing Superior Excel | Superior Visualizations- PIVOT Charts, Sparklines, Waterfall Charts Knowledge Evaluation ToolPak – Regression in Excel |
Module 8: Knowledge Visualization Utilizing Tableau | Tableau vs Excel and PowerBI Exploratory and Explanatory Evaluation Getting began with Tableau Visualizing and Analyzing knowledge with Tableau – I Visualizing and Analyzing Knowledge with Tableau – II Numeric and String capabilities Logical and Date capabilities Histograms and parameters Prime N Parameters and Calculated Fields Dashboards – II and Filter Actions |
Module 9: Python Programming | Putting in Anaconda & Fundamentals of Python Introduction to programming languages Getting Began With Python Introduction to jupyter Notebooks Understanding what are capabilities Defining and calling capabilities Native and world variables Various kinds of arguments Map,scale back,filter,lambda and recursive capabilities Knowledge Buildings in Python Operator Enter and Output Totally different Arithmetic , logical and Relational operators Break , proceed and Cross assertion Record and dictionary comprehensions Understanding what are capabilities Defining and calling capabilities Native and world variables Various kinds of arguments Map,scale back,filter,lambda and recursive capabilities Totally different perform in file dealing with (open,learn, write,shut) Totally different modes (r,w,a,r+,w+,a+) Exception Dealing with, OOPX & Regex What’s exception dealing with Attempt, besides, else and eventually block Various kinds of Exception Totally different capabilities in Regex |
Module 10: Python For Knowledge Science | Operations Over 1-D Arrays Mathematical Operations on NumPy Mathematical Operations on NumPy II Computation Occasions in NumPy vs Python Lists Pandas – Rows and Columns Groupby and Mixture Features |
Module 11: Knowledge Visualization Utilizing Python- Matplotlib & Seaborn | Introduction to Knowledge Visualisation with Matplotlib Introduction to Matplotlib The Necessity of Knowledge Visualisation Visualisations – Some Examples Knowledge Visualisation: Case Examine Knowledge Dealing with and Cleansing: I Knowledge Dealing with and Cleansing: II Outliers Evaluation with Boxplots Knowledge Visualization with Seaborn Pie – Chart and Bar Chart Revisiting Bar Graphs and Field Plots |
Module 12: Exploratory Knowledge Evaluation | Fixing the Rows and Columns Impute/Take away Lacking Values Fixing Invalid Values and Filter Knowledge Introduction to Univariate Evaluation Categorical Unordered Univariate Evaluation Categorical Ordered Univariate Evaluation Statistics on Numerical Options Bivariate and Multivariate Evaluation Numeric – Numeric Evaluation Numerical – Categorical Evaluation Categorical – Categorical Evaluation |
Module 13: Supervised Studying Mannequin – Regression | Introduction to Easy Linear Regression Introduction to Easy Linear Regression Introduction to machine studying Power of easy linear regression Easy linear regression in python Assumptions of easy linear regression Studying and understanding the information Speculation testing in linear regression Residue evaluation and predictions Linear Regression utilizing SKLearn A number of Linear Regression Motivation-when one variable just isn’t sufficient Shifting from SLR to MLR-new issues Coping with categorical variables Mannequin evaluation as compared A number of Linear Regression in Python Studying and understanding the information Constructing the mannequin I & II Residue evaluation and predictions Variable choice utilizing RFE Business Relevance of Linear Regression Linear regression revision Prediction versus projection Exploratory knowledge evaluation Mannequin constructing – I, II & III |
Module 14: Supervised Studying Mannequin – Classification | Univariate Logistic Regression Discovering the most effective match sigmoid curve – I Discovering the most effective match sigmoid curve – II Multivariate Logistic Regression – Mannequin Constructing Multivariate Logistic Regression – Mannequin Constructing Knowledge cleansing and preparation – I & II Constructing your first mannequin Characteristic elimination utilizing RFE Confusion metrics and accuracy Handbook characteristic elimination Multivariate Logistic Regression – Mannequin Analysis Multivariate Logistic Regression – Mannequin Analysis Metrics past accuracy-sensitivity and specificity Sensitivity and specificity in Python Discovering the optimum threshold Mannequin analysis metrics – train Logistic Regression – Business Purposes – Half I Getting conversant in logistic regression Nuances of logistic regression-sample choice Nuances of logistic regression-segmentation Nuances of logistic impression-variable transformation-I, II & III Logistic Regression: Business Purposes – Half II Mannequin analysis – A re-assessment Mannequin validation and significance of stability Monitoring of mannequin efficiency over time Logistic Regression – Business Purposes – Half II Generally face challenges in implementation of logistic regression Mannequin analysis – A re-assessment Mannequin validation and significance of stability Monitoring of mannequin efficiency over time |
Module 15: Superior Machine Studying | Unsupervised Studying: Clustering Introduction to Clustering Executing Okay Means in Python Introduction to Enterprise Drawback Fixing Case Examine Demonstrationchurn instance Introduction to Resolution Bushes Algorithms for Resolution Tree Development Hyperparameter Tuning in Resolution Bushes Ensembles and Random Forests Time Sequence Forecasting – I (BA) Introduction to Time Sequence Time Sequence Forecasting – II (BA) Introduction to AR Fashions Ideas of Mannequin Choice Mannequin Constructing and Analysis |
Module 16: AI- NLP, Neural Networks & Deep Studying | Historical past and evolution of NLP Corpus and Corpus Linguistics Introduction to the NLTK toolkit Preprocessing textual content knowledge with NLTK Fundamental NLP duties utilizing NLTK (e.g., Half-ofSpeech Tagging, Named Entity Recognition) Stemming and Lemmatization Sentiment Evaluation with NLTK Tokenization and Subject Modeling Bag-of-Phrases illustration Sentiment Evaluation Venture: Introduction to Sentiment Evaluation Sentiment Evaluation utilizing supervised and unsupervised strategies Constructing a Sentiment Evaluation mannequin with Python Evaluating Sentiment Evaluation fashions AI vs Deep Studying vs ML Introduction to Synthetic Intelligence (AI), Machine Studying (ML) and Deep Studying (DL) Purposes of AI, ML, and DL Variations between AI, ML and DL The Idea of Neural Networks Introduction to Neural Networks Layers in Neural Networks Neural Networks – Feed-forward, Convolutional, Recurrent Feed-forward Neural Networks Convolutional Neural Networks Recurrent Neural Networks Purposes of Neural Networks Constructing a Deep Studying mannequin with Python Picture Classification with Convolutional Neural Networks Pure Language Processing with Recurrent Neural Networks |
Knowledge Science Tasks and Assignments
Main Tasks
Buyer Lifetime Worth Calculation: The mission entails calculating the shopper lifetime worth utilizing SQL to grasp the income generated by a buyer over their lifetime.
Buyer Churn Prediction: This mission entails constructing a predictive mannequin utilizing SQL to determine clients who’re prone to churn primarily based on their conduct and transaction historical past.
Interactive Dashboard for E-Commerce Gross sales: The mission entails creating an interactive dashboard utilizing Tableau & SQL to research retail gross sales knowledge, determine tendencies, and make data-driven selections.
Buyer Segmentation Dashboard: This mission entails making a buyer segmentation dashboard utilizing Tableau to determine buyer teams primarily based on demographics, conduct, and buying patterns.
Film Suggestion System: The mission entails constructing a film suggestion system utilizing Python and its libraries reminiscent of Pandas, NumPy, and Scikit-Study. The advice system will counsel films primarily based on person preferences and rankings.
Sentiment Evaluation on Twitter Knowledge: This mission entails analyzing Twitter knowledge utilizing Python and its libraries reminiscent of NLTK and TextBlob to carry out sentiment evaluation and perceive the general sentiment of a specific matter.
Visualizing COVID-19 Knowledge: The mission entails visualizing COVID-19 knowledge utilizing Python and its libraries reminiscent of Matplotlib, Seaborn, and Plotly to grasp the influence of the pandemic on totally different nations and areas.
Visualizing Inventory Market Knowledge: This mission entails visualizing inventory market knowledge utilizing Python and its libraries reminiscent of Pandas, Matplotlib, and Bokeh to grasp the tendencies and patterns in inventory costs over time.
Airbnb Knowledge Evaluation: The mission entails performing exploratory knowledge evaluation on Airbnb knowledge to grasp the patterns within the pricing, availability, and high quality of Airbnb listings in numerous cities.
Bike Sharing Knowledge Evaluation: This mission entails performing exploratory knowledge evaluation on bike sharing knowledge to grasp the utilization patterns of bikes in numerous cities and determine elements that affect bike utilization.
Home Value Prediction: The mission entails constructing a regression mannequin utilizing Python and its libraries reminiscent of Scikit-Study to foretell the costs of homes primarily based on their options reminiscent of location, dimension, and facilities.
Credit score Threat Prediction: This mission entails constructing a classification mannequin utilizing Python and its libraries reminiscent of Scikit-Study to foretell the credit score threat of mortgage candidates primarily based on their credit score historical past and different elements.
Time Sequence Forecasting for Gross sales Knowledge: The mission entails constructing a time collection forecasting mannequin utilizing superior machine studying algorithms reminiscent of ARIMA and LSTM to foretell future gross sales tendencies and determine elements that affect gross sales.
Sentiment Evaluation on Product Evaluations: The mission entails constructing a sentiment evaluation mannequin utilizing NLP methods reminiscent of Phrase Embeddings and Recurrent Neural Networks (RNN) to research product evaluations and perceive the sentiment of shoppers in direction of totally different merchandise.
Segmentation utilizing Deep Studying: This mission entails utilizing superior deep studying methods reminiscent of Totally Convolutional Networks (FCN) and U-Internet to carry out picture segmentation and determine objects in photos.
Machine Translation utilizing Transformers: This mission entails constructing a machine translation mannequin utilizing superior deep studying methods reminiscent of Transformers to translate textual content from one language to a different.
Case Research & Assignments:
- Healthcare Buyer Suggestions Evaluation
- Administration Groups Dashboard Creation
- Retail Retailer Gross sales Report Evaluation
- Software program Agency Worker Knowledge Evaluation
- Industrial Knowledge Units Classification & Comparability
- Charts & Graphs: Frequency Distribution Desk, Pie-charts, Pareto Diagram, Histogram, Scatter Plots, Heatmaps, Bar Graphs and plenty of Extra.
- Affected person Illness Chance Evaluation Utilizing Healthcare Knowledge
- Automotive Mannequin & Menu Merchandise Knowledge Mixture & Configuration Chance Evaluation
- Manufacturing & Product Launch Knowledge Classification & Evaluation
- Buyer Criticism Decision Evaluation Utilizing Regular Distribution Curves
- Product Ranking & Worker Productiveness Evaluation Usign Z-Rating
- New Product Want Evaluation Utilizing Speculation Testing
- Stock Administration & Buyer Segmentation Methods Utilizing Vlook up & Hlook Lookup
- Gross sales Pattern & Staffing Plan Creation utilizing Pivot Tables
- Pricing Technique & Monetary Mannequin Creation Utilizing What if Evaluation
- Gross sales & Operations Dashboard Creation
- Healthcare & Development Reporting Automation Utilizing Macros
- Retail Gross sales Alternative Evaluation Utilizing PIVOT Charts
- Accounting Agency Assertion Evaluation Utilizing Sparklines & Waterfall Chart
- FMCG Advertising and marketing Spend to Gross sales Income Impression Evaluation Utilizing Regression Evaluation
- Transportation Pricing Mannequin Utilizing Regression Evaluation
Knowledge Science Placements
100% * Placement Help Devoted Placement Cell To Assist You Land Your Dream Job
Knowledge science course charges
Grasp Program in Knowledge Science Charges: 1,22,720
Contact Data
Digiperform Company Workplace: C-30, Third Ground, Sector-2, Close to Sec-15 Metro Station, Noida, Uttar Pradesh 201301, India
Electronic mail: contact@digiperform.com
Telephone: +91-8527-611-500
Web site: www.digiperform.com
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