Extended CV

Numerical linear algebra

• Linear systems of equations
• Eigenvalue problems
• Linear programming (linear optimization)
• Techniques for large, sparse problems

Numerical analysis

• Function evaluation
• Automatic and numerical differentiation
• Interpolation
• Approximation (Padé, least squares, radial basis functions)
• Integration (univariate, multivariate, Fourier transform)
• Special functions
• Nonlinear systems of equations
• Optimization = nonlinear programming
• Techniques for large, sparse problems
• Numerical data analysis (= numerical statistics)

Linear Algebra, Numerical Analysis and Optimization

• Singular value decomposition (SVD)
• Moore–Penrose inverse
• Condition number
• Accuracy and stability of numerical algorithms
• LU decomposition
• Partial pivoting
• Cholesky decomposition
• Sparse Factorization
• Fill-in
• Least-squares function approximation
• Gaussian elimination
• QR decomposition and pivoting
• Householder transformation
• Givens method
• Eigenvalues and eigenvectors
• Introduction to perturbation theory
• Numerical methods for linear least squares
• Ill-posed problem
• Spectral theorem
• Hessenberg matrix
• QR algorithm
• Jacobi method
• Jacobi eigenvalue algorithm
• Inverse iteration
• Power iteration
• Rayleigh quotient iteration
• Krylov subspace
• Method of Arnoldi, Lanczos, Conjugate gradient, IDR(s) (Induced dimension reduction), GMRES (generalized minimum residual), BiCGSTAB (biconjugate gradient stabilized), QMR (quasi minimal residual), TFQMR (transpose-free QMR), and MINRES (minimal residual) methods.
• Preconditioning
• Incomplete Cholesky factorization

Computational Linear Algebra with Python

• Matrix and Tensor Products
• Matrix Decompositions
• Accuracy
• Memory use
• Speed
• Parallelization & Vectorization
• Topic Frequency-Inverse Document Frequency (TF-IDF)
• Singular Value Decomposition (SVD)
• Non-negative Matrix Factorization (NMF)
• Stochastic Gradient Descent (SGD)
• Intro to PyTorch
• Truncated SVD
• Load and View Video Data
• SVD
• Principal Component Analysis (PCA)
• L1 Norm Induces Sparsity
• Robust PCA
• LU factorization
• Stability of LU
• LU factorization with Pivoting
• History of Gaussian Elimination
• Block Matrix Multiplication
• Broadcasting
• Sparse matrices
• CT Scans and Compressed Sensing
• L1 and L2 regression
• Linear regression in sklearn
• Polynomial Features
• Speeding up with Numba
• Regularization and Noise
• Normal equations and Cholesky factorization
• QR factorization
• SVD
• Timing Comparison
• Conditioning & Stability
• Full vs Reduced Factorizations
• Matrix Inversion Unstabiity
• SVD
• DBpedia Dataset
• Power Method
• QR Algorithm
• Two-phase approach to finding eigenvalues
• Arnoldi Iteration
• Gram-Schmidt
• Householder
• Stability Examples

Nonlinear Dynamics: Mathematical and Computational Approaches

• Maps and difference equations
• Transients and attractors
• Parameters and bifurcations
• Return maps
• Bifurcation diagram
• Feigenbaum and universality
• State variables and state space
• Nonlinearity and nonintegrability
• Fixed points and stability
• Saddle points and eigenvectors
• Stable and unstable manifolds
• Attractors, strange and otherwise
• ODEs, vector fiedls and dynamical landscapes
• ODE solvers
• Forward and backward Euler
• ODEs error and adaptation
• Production ODE solvers
• Numerical dynamics and due diligence
• Shadowing and chaos
• Dynamics and state-space deformation
• Lyapunov exponents
• Sections and projections
• Unstable periodic orbits
• Fractals and chaos
• Time-series analysis and the observer problem
• Delay-coordinate embedding
• Topology, diffeomorphisms and reconstruction of dynamics
• Estimating embedding parameters
• Caveats and extensions
• Computing fractal dimensions
• Computing Lyapunov exponents
• Noise and filtering
• Applications

Visualization (2D and 3D computational geometry)

• Parameter estimation (least squares, maximum likelihood)
• Prediction
• Classification
• Time series analysis (signal processing, filtering, time correlations, spectral analysis)
• Categorical time series (hidden Markov models)
• Random numbers and Monte Carlo methods
• Techniques for large, sparse problems
• Numerical functional analysis

Ordinary differential equations (initial value problems, boundary value problems, eigenvalue problems, stability)

• Techniques for large problems
• Partial differential equations (finite differences, finite elements, boundary elements, mesh generation, adaptive meshes)
• Stochastic differential equations
• Integral equations (and regularization)
• Non-numerical algorithms

Financial Mathematics

• leggi di capitalizzazione
• tassi di interesse a pronti e a termine
• valutazione di obbligazioni-scelta di investimenti
• rischio di tasso di interesse, duration, indicatori di variabilità
• teoria dell’immunizzazione
• teoria dell’utilità attesa
• avversione al rischio
• analisi media-varianza
• scelte di portafoglio tra un titolo rischioso e uno privo di rischio
• scelte di portafoglio tra un titolo privo di rischio e N titoli rischiosi
• scelta di assicurazione
• frontiera dei portafogli con titoli rischiosi
• frontiera dei portafogli con un titolo privo di rischio
• scelte di portafoglio e frontiera dei portafogli
• capital asset pricing model
• assenza opportunità di arbitraggio
• teorema fondamentale dell’asset pricing
• arbitrage pricing theory
• albero binomiale
• mapping del rischio: portafoglio azionario, obbligazionario, derivati
• Value at Risk, Expected shortfall
• principali metodologie per la stima del VaR (full evaluation e metodo delta-normal), backtesting del VaR
• funzionamento sistema finanziario
• intermediazione finanziaria e creditizia
• mercato monetario, obbligazionario, azionario, dei cambi
• ruolo della Banca Centrale
• politica monetaria
• gestione di un intermediario finanziario/creditizio
• Seminario su Rapporto Stabilità Finanziaria (BDI)
• bootstrap della curva, tassi equivalenti, tassi a termine (relazione con quelli a pronti),
• valutazione di obbligazioni
• TIR, TAEG
• Rendimento investimento, tasso cedolare, tasso di rendimento (HP, scadenza e aspettative di tasso)
• rischio di tasso di interesse, duration, immunizzazione
• individuazione del grado di avversione al rischio
• costruzione frontiera
• analisi al variare della correlazione, dei titoli, lunghezza delle serie storiche
• determinazione del portafoglio tangente
• scelte di portafoglio e frontiera dei portafogli con grado di avversione al rischio
• albero binomiale: opzioni americane, payoff complessi
• bound di non arbitraggio su opzioni
• copertura tramite strategie con derivati
• stima del VaR, ES: metodi parametrici, simulazione stroica
• metodo delta-normal
• Backtesting del VaR

Python

• Calling functions and defining our own, and using Python's builtin documentation
• Using booleans for branching logic
• Lists: indexing, slicing and mutating
• Loops and list comprehensions
• Strings and dictionaries
• Working with external libraries: imports, operator overloading
• Python syntax
• Statements and expressions
• Control flow
• Data types
• Sequences
• Dictionaries and sets
• Functions
• Generators and iterators
• Classes
• Exception handling
• Input / output
• Complex example
• Modules and Packages
• Namespaces and scoping
• Text processing
• System functions

Machine Learning

• Data exploration
• Model Validation: measure the performance of your model
• Underfitting and overfitting
• Random forest
• Missing values
• Categorical variables
• Pipeline
• Cross-validation
• XGBoost
• Data leakage
• Cases for model insights
• Permutation features
• Partial plots
• SHAP values
• Aggregate SHAP values

Data Visualization

• Line charts
• Bar charts and heatmaps
• Scatter Plots
• Histograms and density plots
• Plot types and custom style

Pandas

• Creating, reading and writing: dataframe and series
• Indexing, Selecting and assigning
• Summary functions and maps
• Grouping and sorting
• Data types and missing values
• Renaming and combining

Feature Engineering

• Baseline model
• Categorical encodings
• Feature generation
• Feature selection

Deep Learning

• Deep learning for Computer Vision
• Convolutions
• Tensorflow and Keras
• Transfer Learning
• Data Augmentation
• Stochastic Gradient Descent and Back-Propagation
• Models without Transfer Learning
• Dropout and Strides for larger models

SQL

• Handling big datasets with BigQuery and SQL
• Select, From, Where
• Group by, Having, Count
• Order by
• As, With
• Joining data
• JOINs and UNIONs
• Analytic Functions
• Nested and repeated data
• Efficient queries

Geospatial Analysis

• Coordinate reference systems
• Interactive maps
• Manipulating geospatial data
• Proximity analysis

Natural Language Process

• Text classification
• Word vectors

Essential Financial Modelling

• https://app.diplomasafe.com/en-US/diploma/d46196ea682da5da04ead17a72cbdbbc3ec77e560/essential-financial-modelling/linkedin_cert

Dataquest - Data Scientist in Python

• Python For Data Science: Fundamentals Course
• Python For Data Science: Intermediate Course

Uipath

UiPath StudioX

• RPA Starter
• Get Started with StudioX
• User Interface Automation with StudioX
• Word Automation with StudioX
• Decisions, Iterations, Scenarios with StudioX
• Error Handling in StudioX
• File and Folder AUtomation with StudioX
• The StudioX Project Notebook
• Outlook Automation with StudioX
• Virtual Automation Bootcamp with StudioX

UiPath Studio

• Introduction to the RPA Developer Role
• Variables, Data Types and Control Flow
• Data Manipulation
• Excel and Data Tables
• UI Interactions
• Selectors
• Project Organization
• Error and Exception Handling
• Debugging
• Thumbnail
• PDF Automation
• E-mail Automation
• Orchestrator for Developers
• Robotic Enterprise Framework Overview

RPA Business Analyst Foundation

• RPA Business Analysis Fundamentals

Responsive Web Design

• Basic HTML and HTML5
• Basic CSS
• Applied Visual Design
• Applied Accessibility
• CSS Flexbox
• CSS Grid
• https://www.freecodecamp.org/certification/istwine/responsive-web-design

WorldQuant University - Scientific Computing and Python for Data Science

https://wqu.thedataincubator.com/certificate/6583903196282880

• Program flow and data structures
• Data structures, algorithms and classes
• Data formats
• Multi-dimensional arrays and vectorization in NumPy
• DataFrame, Series, data ingestion and transformation with pandas
• Data aggregation in pandas
• SQL and Object-Relational Mapping
• Data munging

WorldQuant University - Machine Learning and Statistical Analysis

https://wqu.thedataincubator.com/certificate/4514208225951744

• Introduction to machine learning and Scikit-learn API
• Regression, classification, & model selection
• Feature engineering
• NLP and dimension reduction
• KNeighbors, clustering and ensemble models
• Support vector machines
• Time series analysis and anomaly detection
• Clustering

DataCamp

Data Scientist with Python

https://www.datacamp.com/statement-of-accomplishment/track/4defa4e8ccb7125ecf946043416f25c0e09828a3

Introduction to Python

• Python Basics
• Python Lists
• Functions and Packages
• Numpy
• https://www.datacamp.com/statement-of-accomplishment/course/25e15f6a7968d20b49d29f55eb6e5c4542f88db6

Intermediate Python

• Matplotlib
• Dictionaries & Pandas
• Logic, Control Flow and Filtering
• Loops
• https://www.datacamp.com/statement-of-accomplishment/course/1249f735e9a51c7ea7d95738027f9968fad410d5

Pandas Foundations

• Data ingestion & inspection
• Exploratory data analysis
• Time series in pandas
• https://www.datacamp.com/statement-of-accomplishment/course/6cdcf13b3123653e53a84110551cda4b7ef32ae7

Data Manipulation with Pandas

• Transforming Data
• Aggregating Data
• Slicing and indexing
• Creating and Visualizing DataFrames
• https://www.datacamp.com/statement-of-accomplishment/course/1e50ed013cc19f82e48e664511481dd329b755f0

Merging DataFrames with Pandas

• Preparing data
• Concatenating data
• Merging data
• https://www.datacamp.com/statement-of-accomplishment/course/ab4f829dca07ecce5b5bb7745a6c0ec01508db8f

Introduction to Data Visualization with Matplotlib

• Introduction to Matplotlib
• Plotting time-series
• Quantitative comparisons and statistical visualizations
• Sharing visualizations with others
• https://www.datacamp.com/statement-of-accomplishment/course/20ff32aa696ff597ee51aea37030046b417281a8

Introduction to Data Visualization with Seaborn

• Introduction to Seaborn
• Visualizing Two Quantitative Variables
• Visualizing a Categorical and a Quantitative Variable
• Customizing Seaborn Plots
• https://www.datacamp.com/statement-of-accomplishment/course/b4d85923e5de1257becd517b4b73b89ae848e9db

Python Data Science Toolbox

• Writing your own functions
• Default arguments, variable-length arguments and scope
• Lambda functions and error-handling
• https://www.datacamp.com/statement-of-accomplishment/course/a61dae84fb58c40326ea89e960c249d69914e962

Python Data Science Toolbox 2

• Using iterators in Python
• List comprehensions and generators
• https://www.datacamp.com/statement-of-accomplishment/course/15d388ccf67d4a059f2ed8c173bbfeb6833aa08a

Intermediate Data Visualization with Seaborn

• Introduction to Seaborn
• Customizing Seaborn Plots
• Additional Plot Types
• Creating Plots on Data Aware Grids
• https://www.datacamp.com/statement-of-accomplishment/course/087ee7be07030815d90886634559d7e12be44112

Introduction to Importing Data in Python

• Introduction and flat files
• Importing data from other file types
• Working with relational databases in Python
• https://www.datacamp.com/statement-of-accomplishment/course/aed443aa02c368ff5b256c29162552a8880d52a1

Intermediate Importing Data in Python

• Importing data from the Internet
• Interacting with APIs to import data from the web
• Diving deep into the Twitter API
• https://www.datacamp.com/statement-of-accomplishment/course/81505c3267b1eb11f0bf8d0b950dac77742699d1

Data Cleaning in Python

• Common data problems
• Text and categorical data problems
• Advanced data problems
• Record linkage
• https://www.datacamp.com/statement-of-accomplishment/course/c69b503e0fcf40376d854b95d20f11b5e096ecf0

Working with Dates and Times in Python

• Dates and Calendars
• Combining Dates and Times
• Time Zones and Daylight Saving
• Easy and Powerful: Dates and Times in Pandas
• https://www.datacamp.com/statement-of-accomplishment/course/b3b39c7b9d56f126e7f0e8909ba7b3c84fc808c4

Writing Functions in Python

• Best Practices
• Context Managers
• Decorators
• More on Decorators
• https://www.datacamp.com/statement-of-accomplishment/course/cccd4da54bf1588d0f078fba0579335d1e60b4d1

Exploratory Data Analysis in Python

• Read, clean, and validate
• Distributions
• Relationships
• Multivariate Thinking
• https://www.datacamp.com/statement-of-accomplishment/course/26a9f47ab9b10498957af591bbc281b836c24ea4

Analyzing Police Activity with pandas

• Preparing the data for analysis
• Exploring the relationship between gender and policing
• Visual exploratory data analysis
• Analyzing the effect of weather on policing
• https://www.datacamp.com/statement-of-accomplishment/course/7cf53e3a5ded47ae2d0deadf2668708a7c851f18

Statistical Thinking in Python (Part 1)

• Graphical exploratory data analysis
• Quantitative exploratory data analysis
• Thinking probabilistically: Discrete variables
• Thinking probabilistically: Continuous variables
• https://www.datacamp.com/statement-of-accomplishment/course/2510a5a498d0c9d63ae1acf6beba8287ed4d8f95

Statistical Thinking in Python (Part 2)

• Parameter estimation by optimization
• Bootstrap confidence intervals
• Introduction to hypothesis testing
• Hypothesis test examples
• https://www.datacamp.com/statement-of-accomplishment/course/40190d3fa694e10483416873a4d3f0d73dc320cc

Supervised Learning with scikit-learn

• Classification
• Regression
• Fine-tuning model
• Preprocessing and pipelines
• https://www.datacamp.com/statement-of-accomplishment/course/21e6baa27fd626ae83263c0cd9dafe7a0cda6fac

Unsupervised Learning in Python

• Clustering for dataset exploration
• Visualization with hierarchical clustering and t-SNE
• Decorrelating your data and dimension reduction
• Discovering interpretable features
• https://www.datacamp.com/statement-of-accomplishment/course/e4d104a7675a916c87109b712dd30eb57fad2d08

Machine Learning with Tree-Based Models in Python

• Classification and Regression Trees
• The Bias-Variance Tradeoff
• Bagging and Random Forests
• Boosting
• Model Tuning
• https://www.datacamp.com/statement-of-accomplishment/course/3b7ed51db0388b988a276c94ceeebe543b3df94e

Case Study: Machine Learning in Python

• Exploring the raw data
• Creating a simple first model
• Improving your model
• Learning from the experts
• https://www.datacamp.com/statement-of-accomplishment/course/1425e788e3c6acf17d57371479d4b0c182bae5af

Cluster Analysis in Python

• Introduction to Clustering
• Hierarchical Clustering
• K-Means Clustering
• Clustering in Real World
• https://www.datacamp.com/statement-of-accomplishment/course/84e29075aea6af4231e9bac5740508402ef88fec

Data Analyst with Python

https://www.datacamp.com/statement-of-accomplishment/track/df978de64672fadfd03c563736dfff3716e800b5

Introduction to SQL

• Selecting columns
• Filtering rows
• Aggregate Functions
• Sorting and grouping
• https://www.datacamp.com/statement-of-accomplishment/course/64146da9cee29fcb70a8caeb9f699a1970356f1f

Intermediate SQL

• CASE statements
• Short and Simple Subqueries
• Correlated Queries, Nested Queries, and Common Table Expressions
• Window Functions
• https://www.datacamp.com/statement-of-accomplishment/course/704442b106bede897f692ed2d43f4f72f0163601

Streamlined Data Ingestion with pandas

• Importing Data from Flat Files
• Importing Data From Excel Files
• Importing Data from Databases
• Importing JSON Data and Working with APIs
• https://www.datacamp.com/statement-of-accomplishment/course/13dd56296f5c167e1195a5da3abba3bc903d4018

Introduction to Relational Databases in SQL

• Database
• Enforce data consistency with attribute constraints
• Uniquely identify records with key constraints
• Glue together tables with foreign keys
• https://www.datacamp.com/statement-of-accomplishment/course/e86b4d288c4a4e47668d2d3a26271239a29bcefb

Joining Data in SQL

• Introduction to joins
• Outer joins and cross joins
• Set theory clauses
• Subqueries
• https://www.datacamp.com/statement-of-accomplishment/course/e6aadd798746f0675d4781a4062ce79c4ad5613d

Introduction to Databases in Python

• Basics of Relational Databases
• Applying Filtering, Ordering and Grouping to Queries
• Advanced SQL Queries
• Creating and Manipulating your own Databases
• https://www.datacamp.com/statement-of-accomplishment/track/df978de64672fadfd03c563736dfff3716e800b5

Machine Learning Scientist with Python

https://www.datacamp.com/statement-of-accomplishment/track/2fb743dfe6c9f233c6a524397a0cbbb5dae6633a

Supervised Learning with scikit-learn

• Classification
• Regression
• Fine-tuning model
• Preprocessing and pipelines
• https://www.datacamp.com/statement-of-accomplishment/course/21e6baa27fd626ae83263c0cd9dafe7a0cda6fac

Unsupervised Learning in Python

• Clustering for dataset exploration
• Visualization with hierarchical clustering and t-SNE
• Decorrelating your data and dimension reduction
• Discovering interpretable features
• https://www.datacamp.com/statement-of-accomplishment/course/e4d104a7675a916c87109b712dd30eb57fad2d08

Linear Classifiers in Python

• Applying logistic regression and SVM
• Loss functions
• Logistic regression
• Support Vector Machines
• https://www.datacamp.com/statement-of-accomplishment/course/19330af01adff2de8144113050c6e428e883c010

Machine Learning with Tree-Based Models in Python

• Classification and Regression Trees
• The Bias-Variance Tradeoff
• Bagging and Random Forests
• Boosting
• Model Tuning
• https://www.datacamp.com/statement-of-accomplishment/course/3b7ed51db0388b988a276c94ceeebe543b3df94e

Extreme Gradient Boosting with XGBoost

• Classification with XGBoost
• Regression with XGBoost
• Fine-tuning your XGBoost model
• Using XGBoost in pipelines
• https://www.datacamp.com/statement-of-accomplishment/course/3b7ed51db0388b988a276c94ceeebe543b3df94e

Cluster Analysis in Python

• Introduction to Clustering
• Hierarchical Clustering
• K-Means Clustering
• Clustering in Real World
• https://www.datacamp.com/statement-of-accomplishment/course/84e29075aea6af4231e9bac5740508402ef88fec

Dimensionality Reduction in Python

• Exploring high dimensional data
• Feature selection I, selecting for feature information
• Feature selection II, selecting for model accuracy
• Feature extraction
• https://www.datacamp.com/statement-of-accomplishment/course/05319ba262cd70d2075687ced39405af068963b7

Preprocessing for Machine Learning in Python

• Introduction to Data Preprocessing
• Standardizing Data
• Feature Engineering
• Selecting features for modeling
• https://www.datacamp.com/statement-of-accomplishment/course/a4aed68dc195a266eb8fc65c9e13235f0f95f744

Machine Learning for Time Series Data in Python

• Time Series and Machine Learning Primer
• Time Series as Inputs to a Model
• Predicting Time Series Data
• Validating and Inspecting Time Series Models
• https://www.datacamp.com/statement-of-accomplishment/course/0d24b7297ccf427dec2205b422c4287411177c0f

Feature Engineering for Machine Learning in Python

• Creating Features
• Dealing with Messy Data
• Conforming to Statistical Assumptions
• Dealing with Text Data
• https://www.datacamp.com/statement-of-accomplishment/course/908b14d25c1875215a4cd2b921a90d60394db883

Model Validation in Python

• Basic Modeling in scikit-learn
• Validation Basics
• Cross Validation
• Selecting the best model with Hyperparameter tuning
• https://www.datacamp.com/statement-of-accomplishment/course/b8a1ca7ba4bcb9843811842e387e20cd234f0c2f

Introduction to Natural Language Processing in Python

• Regular expressions & word tokenization
• Simple topic identification
• Named-entity recognition
• Building a "fake news" classifier
• https://www.datacamp.com/statement-of-accomplishment/course/9a5082f9587c06bb0d49993d92b3e7bf5023a438

Feature Engineering for NLP in Python

• Basic features and readability scores
• Text preprocessing, POS tagging and NER
• N-Gram models
• TF-IDF and similarity scores
• https://www.datacamp.com/statement-of-accomplishment/course/5fc08b633beab972d9a8ed010f29d8a791b86c1f

Introduction to TensorFlow in Python

• Introduction to TensorFlow
• Linear models
• Neural Networks
• High Level APIs
• https://www.datacamp.com/statement-of-accomplishment/course/24f47123941dccc8b6b55e0287b307c221203bb1

Introduction to Deep Learning in Python

• Basics of deep learning and neural networks
• Optimizing a neural network with backward propagation
• Building deep learning models with keras
• Fine-tuning keras models
• https://www.datacamp.com/statement-of-accomplishment/course/845a8ecf7dc652b4c9d82061cc29b076fa385e3c

Introduction to Deep Learning with Keras

• Introducing Keras
• Binary, multi-class, and multi-label problems with neural networks
• Improving Model Performance
• Advanced Model Architectures
• https://www.datacamp.com/statement-of-accomplishment/course/2123aac85ff297d8ddbeaf09530d94832c509d1f

Advanced Deep Learning with Keras

• The Keras Functional API
• Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers
• Multiple Inputs: 3 Inputs
• Multiple Outputs
• https://www.datacamp.com/statement-of-accomplishment/course/d9810fe288c78dbe92b0a8e73f70748874128de1

Image Processing in Python

• Introducing Image Processing and scikit-image
• Filters, Contrast, Transformation and Morphology
• Image restoration, Noise, Segmentation and Contours
• Advanced Operations, Detecting Faces and Features
• https://www.datacamp.com/statement-of-accomplishment/course/91c49b07eb2a62a5c1257c3d2ea1efa0c77a05f9

Image Processing with Keras in Python

• Image Processing With Neural Networks
• Convolutions
• Multiple convolutional layers into a deep network
• Understanding and Improving Deep Convolutional Networks
• https://www.datacamp.com/statement-of-accomplishment/course/e5ba3e450e3a1e315c29bd222b125b0981c993f7

Hyperparameter Tuning in Python

• Hyperparameters and Parameters
• Grid search
• Random Search
• Informed Search
• https://www.datacamp.com/statement-of-accomplishment/course/b7b7610776af6814b702b32c15498061e692e61d

Introduction to PySpark

• Introduction to PySpark
• Manipulating data
• Getting started with machine learning pipelines
• Model tuning and selection
• https://www.datacamp.com/statement-of-accomplishment/course/0b2a9e787a061a507a42ee8f4c418760a1670524

Big Data Fundamentals with PySpark

• Apache Spark
• Programming in PySpark RDD’s
• PySpark SQL & DataFrames
• Machine Learning with PySpark MLlib
• https://www.datacamp.com/statement-of-accomplishment/course/b8fdf6f871bece26cdc133f3d3433b2b6883dbb7

Intro to data cleaning with Apache Spark

• DataFrame details
• Manipulating DataFrames in the real world
• Improving Performance
• Complex processing and data pipelines
• https://www.datacamp.com/statement-of-accomplishment/course/c5ad387c02f74848dcac401052567355807890ec

Feature Engineering with PySpark

• Exploratory Data Analysis
• Wrangling with Spark Functions
• Feature Engineering
• Building a Model
• https://www.datacamp.com/statement-of-accomplishment/course/81f3306b821a50a2053f4770f94edfee266d115e

Building Recommendation Engines with PySpark

• Recommendations engines
• Alternating Least Squares algorithm
• Cross-validated ALS model
• Real-life ALS recommendation engine
• https://www.datacamp.com/statement-of-accomplishment/course/a55e7ad416ee97c7868996247df383fbcc422394

Machine Learning with PySpark

• Introduction
• Classification
• Regression
• Ensembles & Pipelines
• https://www.datacamp.com/statement-of-accomplishment/course/d7a86227867044338047764edee1eac109aa7e35

Kaggle Competition in Python

• Kaggle competitions process
• Dive into the Competition
• Feature Engineering
• Modeling
• https://www.datacamp.com/statement-of-accomplishment/course/422886e116b36d4783898a3831213bfba2cc8832

Extra

• https://www.codewars.com/users/istwine/stats
• https://www.hackerrank.com/cvecchi76?hr_r=1