Data Modeling, Data Visualization, DAX (Data Analysis Expressions), Power Query, Dashboard Design, Report Sharing and Collaboration, Data Connectivity and Preparation, Security and Administration, Power BI Service, Mobile Reporting
Python programming, data manipulation with pandas, data visualization with matplotlib and seaborn, probability theory, hypothesis testing, statistical distributions, linear regression, machine learning basics, inferential statistics, exploratory data analysis
Data Mining, Machine Learning, Predictive Analytics, Data Visualization, Natural Language Processing, Cloud Computing, Internet of Things (IoT), Real-time Analytics, Data Governance, Edge Computing
Supervised learning, Unsupervised learning, Neural networks, Convolutional neural networks (CNNs), Recurrent neural networks (RNNs), Natural language processing (NLP), Reinforcement learning, Generative AI, Feature engineering, Transfer learning
Data analysis on canadian immigration project using pandas, numpy, matplotlib, and seaborn
San francisco crime dataset visualization using folium framework
NLTK library and wordcloud library for natural language processing and building data visualization
Regression plots, bar plots, pair plots, facetgrids, boxplots, violin plots using seaborn data visualization on titanic dataset
Data cleaning, outlier removal, normalization on automobile efficiency prediction dataset
Applying one sample hypothesis tests - binomial test, wilcoxon rank test, student T-test and chi square test using scipy, researchpy, statsmodels and sklearn librarys on student statistics and general stat survey datasets
Applying two sample paired and unpaired tests for finding if phenomenon is significant in the sample
Finding which feature is statistically significant and will be retained for prediction
Integrating gpt-3.5 and statsmodels to explain in simple language the results of statistical tests
Applying linear regression and logistic regression on iris flower recognition and mnist handwritten digit recognition
Implement naive bayes and tree models - random forest, boosting, bagging, and ensemble methods
Calibrating classifiers, using pipelines, grid search and random search for hyper-parameter tuning.
Calculating precision, recall, f1-score, confusion matrix and roc-auc.
Projects on satellite image classification, food and traffic signal classification
Applying artificial neural networks for image and text classification and regression
Bias-variance tuning, optimizing networks and regularization
Data augmentation for variance reduction. scrapping tweets from twitter.
Deployment of deep learning app on cloud
Case studies on skin melanoma, audio command recognition, text to image generation and text classification
Project on seed classification, imdb movie sentiment analysis
Postgresql for basic, intermidiate and complex queries for extracting data from world atlas, hotel management and attendance management databases
GPT-3.5 api to build websites and deploy on the streamlit cloud through github ci/cd pipeline
Dashboard building, power bi report and features
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Tier-I engineer
₹20000(120 hours)
Tier-II engineer
₹20000(120 hours)
Tier-III engineer
₹40000(240 hours)