Complete Road Map for Data Science & ML for Begineers

Explore probability and statistics from basic to advanced, solving practical problems on dice, averages, draws without replacement, standard deviation, variance, defectives, and outliers in machine learning contexts.

Explore fundamental algebra concepts through worked examples: solving linear equations, factoring quadratics, solving systems by substitution or elimination, and finding nth terms in arithmetic sequences and series.

Explore basic to advanced geometry through practical problems, including area calculations, right triangles, circle circumference, and angle properties of parallelograms and hexagons.

Apply arithmetic series formulas to sum even numbers 1 to 20, use lcm to find the smallest multiple of 3 and 5, and count palindromes between 100 and 1000, 90.

Explore basic to advanced probability and combinatorics concepts, including letter arrangements in apple, two-card ace probabilities from a 52-card deck, ten choose three, and inclusion-exclusion on the Mississippi problem.

Explore how to identify and apply common sequence patterns—arithmetic, geometric progressions, alphabet sequences, square numbers, and shapes—essential skills for data science and ml beginners to solve missing-term problems.

Learn how to solve time and speed problems by applying distance equals speed times time, compute average speed, and handle multi-leg scenarios like opposing cars and plane trips.

Solve age problems from basic to advanced using simple relationships and systems of equations, with examples like John and Jane, Alex and Ben, and Alice and Bob.

Apply ratio and proportion to speed, distance, time, and average speed problems. Use cross-multiplication and basic equations to model cookies, travel, mixtures, and data science scenarios.

Learn how to work with logarithms across bases, rewrite numbers as powers, and apply log rules such as log a minus log b = log(a/b) to solve equations.

Explore set theory fundamentals with cardinality of the empty set, intersections, unions, and complements using Venn diagrams, including practical examples and distribution laws.

Explore foundational probability and distributions using coin tosses, dice, card draws, and normal distribution concepts, including z-scores, with practical reasoning and coding-decoding and sequencing problems.

Explore Python data structures by examining lists and arrays, indexing techniques, slicing with start, end, and step, and how append and extend add elements.

Master Python list operations, including append, extend, insert, and remove. Explore indexing, searching with in, nested lists, tuples, and basic NLP concepts like bag-of-words and TF-IDF.

Explore how bag of words and tf-idf indexing drive text search in python, using multi-dimensional lists, tuples, and dictionaries. Learn to manage corpora and prepare word embeddings for NLP.

Explore set operations and joins, including union, left join, right join, outer join, and inner join, to analyze the frequency of words and connect with embeddings and transfer learning.

Master dictionaries as key-value stores with hash tables, update and delete by key, and explore stop words, stemming, frequency, and how lists and tuples relate to dictionaries.

Explore string manipulation in Python for NLP, covering case sensitivity, spaces, slicing, palindrome checks, substring search, and tokenization for embedding matrices.

Convert raw date time features into usable formats, extract day of week, month, year, hour, and minutes, and adapt to varying formats and Unix time for preprocessing and modeling.

Explore churn prediction in a bank case study using logistic regression, focusing on data loading, preprocessing, imputation, feature engineering, and model evaluation for production-ready deployment.

Explore lambda expressions in Python, learning how to define one-line functions for iterative operations and simple arithmetic, and use filter to select items such as odd numbers.

Learn to use Python’s filter, map, and reduce with lambda functions to transform lists and strings. Apply basic NLP preprocessing, including tokenization, and perform common aggregation and string operations.

Learn how to declare local and global variables, define and call functions, pass arguments, and return values to build reusable Python code for simple computations.

Explore Python functions and parameter passing, including fixed, positional, and keyword arguments, with *args and **kwargs, while clarifying local and global variable scope.

Explore global variables, map function mechanics, and string capitalization techniques; compare iterative and recursive approaches through a handshakes example and fibonacci-style recursion, highlighting memory use and returns.

Learn to handle diverse date time formats in Python by converting dates in a data frame and extracting hours, minutes, day of week, day of year, quarter, and month end.

Learn to evaluate classification models using KPI metrics like confusion matrix, accuracy, precision, recall, F1 score, specificity, sensitivity, and the AUC ROC curve for supervised binary and multi-class tasks.

Learn core math and random libraries, perform seal, floor, factorial, and gcd operations, generate random numbers, and format dates with date time directives for data cleaning and plotting workflows.

Navigate python notebooks with os operations to manage work directories and load data for EDA. Use numpy, pandas, and matplotlib to describe college football statistics from Kaggle with basic metrics.

Identify data types and convert non-numeric features for modeling, then explore correlations through heatmaps to detect high collinearity and guide feature selection and feature importance.

Remove outliers and visualize data with scatter, strip, box, joint, and region plots to explore distributions, time series features, and iqr insights using Pandas and NumPy.

Extract the month from timestamps and one-hot encode month and country to build a dataset for predicting ad clicks 0 or 1 with a 70/30 split.

Explore logistic regression for classification and regression, with sigmoid activation. Learn model fitting, hyperparameter tuning, and metrics like AUC ROC, precision, and recall on training and test data.

Explore logistic regression as a classification technique used for churn prediction, using non-linear transformations and the logistic curve, with geometric, probability, and loss minimization interpretations.

Explore logistic regression with the sigmoid function, address outliers and scale differences, and compare L1 and L2 regularization (elastic net) to prevent overfitting.

Explore loss interpretations in logistic regression, including log loss and hinge loss, and how sigmoid curves shape model training and SVM with soft and hard margins.

Preprocess numerical features with imputation and scaling to 0-1, then train a logistic regression model in scikit-learn, evaluating with F1, accuracy, and log loss.

Learn data preprocessing for logistic regression: impute missing values with knn, one-hot encode categorical variables, scale features, and evaluate with five-fold cross-validation using AUC and recall.

Learn how five-fold cross-validation with backward feature elimination and forward feature selection enhances logistic regression, boosting AUC ROC and accuracy with top features and hyperparameter tuning in scikit-learn.

Explore nlp fundamentals, including tokenization and converting text to numeric vectors, using bag-of-words, tf-idf, and word vectors, with hands-on eda on coronavirus datasets.

Explore text representations from bag of words and tf-idf to word2vec, highlighting semantic limits, stop words, and practical use with scikit-learn for NLP tasks; discover transfer learning and pretrained vectors.

Explore transfer learning and word vector techniques by averaging vertovec and tf-idf weighted vertovec, and visualize data with Matplotlib using Covid-19 datasets from Kaggle.

Learn to analyze and visualize statewise covid data with Python, using bar plots, color variations, and calculated death-to-case ratios; sort top states to compare trends for beginners.

Learn to visualize and interpret correlations with heatmaps and a correlation matrix, identify highly correlated features, and explore data concepts like scatter plot, time series, Gaussian distribution, and Python analytics.

Explore the machine learning lifecycle from problem definition and low-latency constraints to data selection, preprocessing, tokenization, eda with matplotlib and seaborn, model training, evaluation, and deployment on cloud platforms.

Deploy and monitor machine learning models through an iterative lifecycle, refining feature selection, data preprocessing, and text processing with tokenization to improve precision.

Apply lemmatization and stemming for text preprocessing, then create a binary sentiment model using tf-idf and bag-of-words on amazon reviews from kaggle, with sql and sqlite pipelines.

Learn practical data preprocessing for structured and text data, including deduplication, missing value handling, outlier detection, and html and url removal using regular expressions and stop words and stemming.

Explore text pre-processing and feature extraction using bag of words, count vectorizer, unigrams and n-grams, tf-idf, and word2vec, then apply logistic regression and evaluate with AUC.

Apply logistic regression with a 70/30 train-test split, tune C and lambda, compare L1 and L2 regularization, and assess AUC via ROC curves, plus explore bag-of-words, tf-idf, and word vectors.

Explore end-to-end sentiment analysis on reviews with preprocessing, deduplication, and HTML removal, then compare bag-of-words, TF-IDF, and Vertovec features using logistic regression.

Compare l1 and l2 regularization on text classification with tf-idf and bag-of-words features. Analyze train and test roc-auc, confusion matrices, and feature importance while discussing hyperparameter tuning and overfitting.

Explore various classification and regression models—logistic regression, SVM, decision trees, random forests, linear regression, regularization (L2), and SVR—evaluating with TF-IDF, word2vec, and bag-of-words, while emphasizing AUC, confusion matrices, and cross-validation.

Minimize squared linear regression error by fitting a hyperplane with w and intercept w0, using y - y_hat squared loss and regularization (L1/L2) to handle outliers.

Explore linear regression and logistic regression, address multicollinearity and interpretability, apply standardization and normalization, and use a Boston house prices dataset with train-test splits in scikit-learn.

Explore how L1 and L2 regularization influence linear regression with ridge and lasso, compare scikit-learn and custom implementations, and analyze errors and Gaussian distribution for outliers.

Explore the decision tree classifier, its use for classification and regression tasks, root and internal nodes, yes/no splits, and leaf nodes with majority voting.

Develop a decision tree by splitting data using entropy, information gain, and gini impurity. The lecture uses an outlook play tennis dataset with features like outlook, temperature, humidity, and wind.

Explore information gain, entropy, and gini impurity for decision trees. Learn how to compute weighted average entropy, partition data, and compare entropy and information gain for model interpretability.

Demonstrates majority vote for classification, explains decision tree regression with mean squared error and mean absolute error, and covers depth, overfitting, underfitting, and label encoding with Python.

Understand why one hot encoding is preferred over label encoding for categorical data, and how feature binning with regions plus a decision tree helps reduce dimensionality and overfitting.

Explore decision trees in a scikit-learn workflow, covering entropy, information gain, and Gini impurity, with techniques for imbalanced data, class balance, and max depth tuning.

Explore random forest, bagging, and bias-variance trade-offs, with hands-on examples of random sampling, bootstrap, and model aggregation. Learn to deploy ml with flask, ui templates, and a loan prediction app.

Explore NLP sentiment analysis on a movie reviews dataset, using tokenization, bag of words, tf-idf, word2vec, and bert, with data cleaning, imputation, and univariate to time series analysis.

Explore distribution and cumulative plots for loan funded amounts, interest rates, instalment, and dti. Identify how box plots and time-series reveal loan status patterns, guiding decisions.

Analyze correlations with a heatmap to reveal relationships between loan amount, installment, interest rate, and income. Apply univariate and bivariate plots, use segmented plots to explore, and discuss feature engineering.

Explore univariate analysis and segmented plots to visualize loan status, interest rates, and charge-off trends in a unified data frame.

Explore bivariate and multivariate analysis in R, using box plots, scatter plots, and cat plots to examine how grade and interest rate relate to loan status and funded amount.

Explore how to detect outliers in loan data and compare charged-off versus fully paid statuses across 36- and 60-term loans, using pivot tables and heat maps.

Explore data forecasting and time series analysis, learn k-means for unsupervised classification, and compare regression and classification within supervised learning, with time-based train-test splits.

Forecast time series and seasonal patterns with ARIMA, SARIMA, moving averages, AR models, Prophet, and LSTM for NLP. Explore unsupervised learning with iris data using k-means, hierarchical clustering, and DBSCAN.

Explore k-means clustering by initializing centroids, assigning points to nearest centroids using Euclidean distance, and updating centroids until convergence. Learn about initialization choices, k as a hyperparameter, and outlier handling.

K-means++ uses probabilistic initialization based on distance to centroids to handle outliers and improve clustering; the elbow method selects the optimal number of clusters by within-cluster sum of squared distances.

Apply k-means clustering with elbow method and k-means++ initialization, updating centroids, and compare intra-cluster and inter-cluster distances using the iris data set.

Analyze iris data using k-means with k-means++ and random initialization, compare iterations and CPU time, and explore hierarchical clustering, decision-tree concepts, and practical small vs large data set trade-offs.

Explore a flight fare prediction project using a Kaggle dataset, outlining data cleaning, preprocessing, feature exploration (date of journey, source, destination, route, stops, duration), and initial visual analyses.

Develop feature engineering for flight data by extracting depart and arrival hours, minutes, and date; encode stops and airlines, then train xgboost and lightgbm with cv, deploying via flask pickle.

Launch a flight price prediction app using a trained XGBoost model, a Flask UI with get and post methods, and inputs like date, departure time, arrival time, stops, and airline.

Explore relational database concepts with MySQL, including SQL queries, RDBMS, data storage, data lake, and normalization; understand entity integrity, domain integrity, referential integrity, and user defined integrity.

Explore data definition language, data manipulation language, and transaction control language concepts; learn table design, constraints, referential integrity, and basic DDL/DML commands through hands-on examples.

Explore DDL, DML, and TCL, and how schemas, constraints, and data types shape relational data. Examine one-to-one, one-to-many, and many-to-many relationships, and authorization basics in Power BI contexts.

Learn SQL fundamentals with DDL and DML, including create, alter, and update; explore slowly changing dimension type two, assertions, and integrity constraints through a MySQL sample database.

Explore the basics of sql, including table structure and keys. Learn ddl commands like alter, drop, and rename, dml concepts, and nested queries for beginners.

Explore data definition, manipulation, and transaction control languages, focusing on DDL schemas, data types, and constraints; cover referential integrity and one-to-one, one-to-many, and many-to-many relationships, plus authorization.

Explore how read, insert, and update authorizations control access to a MySQL database, including SCD type two and DDL commands like create, alter, drop, and rename.

Explore sql fundamentals by creating tables with the create table syntax, define columns and data types, and apply primary keys while learning ddl and dml concepts and one-to-many relationships.

Explore DDL basics, including keys, primary keys, and not null constraints. Learn to alter tables, add or modify columns, drop columns, and rename tables.

Master renaming in sql using the basic syntax, rename and replace with table references, and explore dml, transaction control language, nested queries, and database management system for beginners.

Explore data manipulation language fundamentals, including declarative versus procedural DML, and learn to retrieve, insert, update, and delete data using SQL commands like SELECT from an employee table.

Master SQL data manipulation basics: insert, copy rows between tables, and update or delete records with where conditions. Explore using in and between ranges to target employee and dependent IDs.

Learn sql basics to insert values, copy rows between tables, and update or delete records with where clauses, using in and between to filter employee and dependent ids.

Master transaction control language in SQL with commit, rollback, savepoint, and explore data control language using grant and revoke, plus domain constraints and not null integrity constraints.

Explore domain and check constraints, not null and unique requirements, on employee and user tables, and learn to define primary keys, foreign keys, and alter constraints.

Learn to define not null constraints, foreign keys, primary keys, and check constraints, alter and update tables, and enforce data validity with current date, selling price, and cost relations.

Learn how primary keys enforce uniqueness and non-null constraints, create composite keys, and use foreign keys to link tables and perform joins in SQL.

Learn to create and enforce foreign key constraints between tables like projects and milestones, use alter table to add or drop constraints, and understand truncate vs delete in MySQL.

Examine when to use delete versus truncate, the role of triggers, and practical loading of 10,000 rows into a big data table, plus union, union all, and intersect.

Learn SQL filtering techniques using the where clause to retrieve or exclude data efficiently, using operators like =, !=, >, <, >=, <=, and handle exists, null, and date formats.

Filter data with the where clause and compare operators, select employee id, name, and salary, then use having with group by for aggregate salaries and order by salary.

Learn SQL querying for salary data: filtering with where, between and in, aggregating with sum, min, and max, and using having, group by, and exists with and or.

Explore how sql conditions and operators (and, or, null values) shape queries, with true/false outcomes and student data examples, leading into aggregation and group by concepts.

Explore grouping operations in SQL, using group by and order by to categorize data, apply aggregation functions, and count per department or fruit with practical examples.

Master SQL data analysis with group by and order by, compute min, max, and average salaries by department, apply having filters, and explore distinct, joins, and analytics concepts.

Explore sql syntax for data science beginners, including aliases without keywords, between and group by, ordering results, is null and like with wildcards, plus limit and offset.

Explore sql pattern matching with like and not like, handle nulls with isnull and null concepts, and learn grouping sets and basic table creation with primary key and foreign key.

Learn how sql joins connect two tables using primary keys, exploring inner, left, right, and full outer joins, their data retrieval behavior, and handling nulls.

Explain how left and right joins produce nulls and the limitations of left joins. Use aliases with as and on to rename columns while querying employee and department data.

Explore SQL joins including inner, left, right, full outer, and cross join, using examples with employees, departments, country, and locations to show null values and aliasing.

learn how to use rank, dense_rank, and row_number in sql with practical car data examples. explore over partitions by company, descending order by power, handling ties, and limitations of rank.

Learn how rank, dense_rank, and row_number work with over partitioning by company and power and order by power descending, and compare their behavior with ties and unique sequencing.

Discover how views act as virtual tables in sql, storing queries and enabling selective columns and joins. Master creating, replacing, and dropping views to prepare data for analytics.

Explore SQL triggers, including before and after events, insert/update/delete actions, row-level versus statement-level triggers, and automatic monthly executions tied to a table.

Learn how to implement SQL triggers to log salary changes for employees using a before update trigger, capturing old and new salaries with current timestamps in the salary_changes table.

Learn how nested subqueries execute inner queries first to drive outer queries, using employees and departments examples, and review basic syntax, operators, and exists and not exists checks.

Explore exist and not exist, any and all in subqueries, and learn how stored procedures save and reuse SQL code with a simple customers demo.

Learn to fetch customer data by city using where clauses and parameters, implement storage procedures, and create and manage indexes (including unique and composite) to speed data retrieval.

Explore sequence-to-sequence models and word embeddings in Python, focusing on encoder-decoder architectures, GRU, LSTM, and RNN units, context vectors, and attention for machine translation and NLP.

Use PyTorch to build a sequence-to-sequence encoder-decoder for German-to-English translation with Spacy tokenization, trained with Adam and cross-entropy on a toy dataset.

Explore word embeddings with word2vec, fasttext, and glove through data loading, preprocessing, tokenization, and model training using Gensim and NLTK in a PyTorch sequence to sequence context.

Explore an end-to-end nlp pipeline using nltk, gensim, and spacy to build word embeddings and sequence-to-sequence models on twitter data; design preprocessing, n-gram methods, smoothing, and an autocomplete system.

Explore Excel from basics to advanced, covering workbook types, csv/tsv/xlsx/xlsb formats, core operations, formulas, and cell-level formatting, plus UI essentials, sheets, and data visualization options.

Navigate the Excel user interface, exploring page layout and page break preview, zoom and scroll bars, and the ribbon with formatting, alignment, autosave and undo/redo, plus mouse or touch modes.

Navigate Excel workbooks with file and home tabs, templates, and new workbook creation. Pin, search, share, save, export, and print using the formula box, name box, and status bar.

Master spreadsheet formatting for data analysis by applying fonts, borders, fills, alignment, rotation, wrapping, merging, and currency and percent formatting, plus conditional formatting and data bars.

Explore gradient and solid fills, cell formatting, pivot tables, charts, 3D maps, filters, and quick analysis like sum and average on a sample sales dataset.

Learn to analyze sales data for data science, using color coding, max-value highlighting, and formulas for sum and average, then sort, filter, and format month- and year-wise views.

Explore lookup concepts alongside essential formulas for sales data, such as count, sum, average, max, min, and product, and practice range specification using a cell or drag to extend.

Explore essential spreadsheet operations for data analysis, including finding max and min, calculating sum and average, counting cells, and using ranges with if-else logic and date functions.

Learn how to use count, count blank, count if, and if in spreadsheet ranges to count non-empty cells, blanks, or cells meeting a condition, with practical examples.

Learn to handle conditions and date time checks with convert and today functions, display today’s date and current time, and perform unit conversions like centimeter to meter.

Learn to use paste special and format cells shortcuts, and customize numbers, decimals, alignment, fonts, borders, colors, and protection in spreadsheets.

Learn to create tables and pivot tables, navigate ribbon tabs, and use drawing tools to sketch shapes, then convert table elements and apply pie, triangle area formulas, x^2, and y^2.

Explore how to customize Excel sheets with themes, colors, and fonts, and master page layout options, margins, orientation, scales to fit, grid lines, breaks, print titles, and previewing for print.

Create and format a data table in a spreadsheet with gridlines, headers, and page layout. Explore table design for duplicates, pivot summaries, and exports (CSV or SharePoint) with filters.

Discover how Excel learns patterns by dragging to generate sequences, from numbers to months and weekdays.

Explore practical data table management in spreadsheets, including creating tables, naming columns, removing duplicates, filtering, sorting, pivot tables, and formulas such as address and offset.

Explore Excel logical operators, including is blank, is error, is number, is odd, is even, and is exist, and learn Vlookup fundamentals with table array, column index, and range lookup.

Compare vlookup, hlookup, and xlookup by demonstrating how a row index or range lookup returns values from specified table ranges, such as apple prices, with true and false match rules.

Learn to use index to retrieve values from a data frame by row and column numbers, work with ranges like B3 to E5, and apply the sum operator.

Learn to use match to locate positions in lists or data frames, apply offset to ranges, and leverage indirect to reference cells from strings with A2, A3, and A4.

Learn how ROWS returns the number of rows in a reference or array, with A1 and A1:A4, and how COLUMNS counts columns using A1 to D1.

Explore logical formations and look up in Excel, mastering and, or, not operators, truth tables, and if statements to evaluate true or false conditions.

Explore logical formations and lookup in Excel, using truth tables and and/or operators to show true only when both values are true, with basic syntax for formulas.

Explore advanced AWS SageMaker for end-to-end machine learning model development, training, and deployment, with notebooks, data labeling, and optimization techniques across image recognition, NLP, and predictive analytics.

Learn to set up AWS SageMaker notebook instances and manage data preparation: from importing and exporting to preprocessing, visualization, and quality checks, plus feature selection, hyperparameter tuning, and model evaluation.

Explore how SageMaker handles model training, debugging, and distributed training, comparing pre-trained and custom models, and use S3, Python SDK, and CloudWatch for real-time analysis and endpoint deployment.

Learn to deploy models with SageMaker endpoints, monitor performance and drift with CloudWatch, and leverage pipelines, auto scaling, ground truth labeling, and security practices for cost-efficient ml deployment.

Learn AWS SageMaker best practices, hyperparameter tuning, and feature engineering to build robust ML models, manage costs, and deploy endpoints through case studies and capstone insights.

Explore Amazon SageMaker's capabilities for building, training, and deploying machine learning models at scale using Studio notebooks, autopilot, and integrated data pipelines.

Load the iris data set in Google Colab using scikit-learn and pandas, perform a train/test split, and train a random forest classifier with 100 estimators, saving iris_model.pkl for SageMaker.

Build and deploy machine learning models with AWS SageMaker, serialize with pickle or Joblib, expose endpoints via Flask or Django from Google Colab API keys, covering supervised, unsupervised, reinforcement learning.

Explore data using Databricks to analyze raw datasets, clean and transform data, and visualize results with Spark and SQL, enabling data-driven decisions and machine learning predictions.

Explore how databricks enables end-to-end data science workflows across ecommerce, healthcare, and more, using notebooks for interactive analysis, feature stores, auto ml, hyperparameter tuning, and production pipelines.

Explore real-time IoT scenarios and sensor data for predictive maintenance and exploratory data analysis using Spark SQL Fundamentals in Databricks for structured data processing and visualization.

Analyze data in Databricks with spark sql, joins, aggregations, and window functions, using ETL to visualize traffic patterns and customer demographics.

Explore how Databricks on Azure enables data science and data engineering workflows, integrating blob storage, data lake, data warehouse, Kafka, and Spark to build pipelines and Power BI visualizations.

Explore how to access Databricks via a 14-day free trial, navigate the interface, manage workspaces, data, and storage, and review usage, AWS SSO and S3 bucket integrations.

Boost data warehouse performance with advanced optimization and udf-driven processing on Databricks, leveraging Spark, partitions, and parallelism for fast analytics with Power BI and Tableau.

Discover how to optimize queries, partition and compress data, and streamline ETL processes to boost performance and cut storage costs in large data ecosystems.

Explore how etl, query optimization, and data workflows enhance logistics, manufacturing, inventory, and retail operations, leveraging user defined functions in data warehouses for fast, personalized product recommendations.

Explore how user defined functions enable advanced data transformations in SQL, including custom aggregations and window functions, for data enrichment, ML model training, and case studies across marketing, manufacturing, healthcare.

Master scaling and resource management in data warehouses to handle volumes and workloads. Use resource allocation, load balancing, and cluster optimization for transaction processing and analytics in e-commerce and finance.

Explore storage and data management fundamentals in the data bricks platform, and learn how data engineers build pipelines with Spark, Kafka, and cloud services for scalable analytics.

Configure virtual machine storage and manage disks to optimize data lake and data warehouse use, applying data governance, lineage, and Delta Lake real-time analytics for IoT and finance.

Discover delta live tables for real-time fraud detection and social media analytics, ensuring data integrity and governance, with backfill handling, disaster recovery, and auto loaders for diverse sources.

Explore how auto loaders automate data ingestion, improve data quality, and streamline backups and snapshot management in data lakes, enabling reliable multi-hop data pipelines for analytics and machine learning.

Explore devops and mlops for data science, containerize models, and implement CI/CD pipelines while managing infrastructure as code, configuration, and production monitoring.

Explore how devops accelerates data science workflows with CI/CD and infrastructure as code. Improve collaboration among data scientists, engineers, and operators while enabling continuous monitoring and feedback.

Apply reproducibility and version control through DevOps and MLOps in data science, enabling collaboration between data scientists and data engineers, and automating CI/CD pipelines with Docker and cloud endpoints.

Explores docker, singularity, kubernetes, and portman containers for building and deploying data science apps, and sets up ci/cd with containerized workflows and devops practices.

Monitor production containers with Kubernetes, Prometheus, and Grafana to optimize performance and resource use, while rolling updates and CI/CD with GitHub, Jenkins, and Docker.

Navigate productionization challenges in the DevOps lifecycle, emphasizing isolation of dependencies and containerized environments for reproducible builds. Employ git and Docker workflows to manage code, containers, and pipelines.