Data Science for Finance Professional Certificate (NYIF)

Colleagues, in the Data Science for Finance Professional Certificate program from the New York Institute of Finance you will master Python Basics, Numerical Programming with NumPy, Plotting with Matplotlib, Scientific Computing with SciPy, Data Analysis with Pandas, SQL Databases, Machine Learning Algorithms I, Machine Learning Algorithms II, Tuning Algorithms, Learning and Clustering, and Neural Networks with Tensorflow. Skill-based training modules include: 1) Review of Python Basics - Variables & Types, Python Lists, List Manipulations, Functions, Methods, Importing Packages, The NumPy Package, NumPy Arrays, Basic Statistics in Python, 2) Numerical Programming with NumPy - Multi-dimensional Arrays, Array Operations, Array and Boolean Indexing, Broadcasting, Vectorizing Code, Generating Random Numbers, Application: Simulating Stochastic Processes, 3) Plotting with Matplotlib - Pyplot for MATLAB Style Plotting, Scatter Plots, Histograms, Box Plots, Financial Plots, Application: Technical Analysis of Stocks, 3D Plotting, Application: Visualizing Volatility Surfaces, 4) Scientific Computing with SciPy - Multi-dimensional Arrays, Array Operations, Array and Boolean Indexing, Broadcasting, Vectorizing Code,  Generating Random Numbers, Application: Simulating Stochastic Processes, 5) Data Analysis with pandas - Dataframes, Series and Panel Objects, Operations, Selecting and Slicing Data, Plotting, Application: Working with Financial Time Series, Grouping Data, Joining, Appending and Merging Data, Application: Portfolio Analysis, 6) SQL Databases - Variety of SQL Databases, sqlite, Python Database API, Connection Objects, Cursor Objects, Row Objects, SQL Basics: Select, Update, Delete, Insert, Joins, Databases, Tables, and Indexes, Create, Alter, and Drop, 7) Machine Learning Algorithms I - Parametric vs Non Parametric Model, OLS Regression, Lasso and Ridge, Extending Parametric Models, Polynomials, Scaling, Subset Selection, Classification Algorithms,  Logistic Regression, L1 and L2 Penalty, Single and Multi-Class, Application: Multi Class Modeling, 8) Machine Learning Algorithms II - Non Parametric Models, Decision Trees, Support Vector Machines, Assembling Methods, Boosting, Adaboost Algorithm, Bagging, Random Forest Algorith, Latest Advances, Extreme Gradient Boosting (XGB), 9) Tuning Algorithms - Cross Validation and Testing, Pipelines and GridSearch, Labs, Regression Practice, Classification Practice, 10) Learning and Clustering - Supervised vs. Unsupervised Learning, Principal Components Analysis, K Means Clustering, DBSCAN Clustering, and 11) Neural Networks with Tensorflow - Introduction to Neural Networks, Specifying a Model in Tensorflow, Training and Testing a Model, and Application: Predictive Modeling in the Financial Markets.

Enroll today (teams & executives are welcome): https://tinyurl.com/2jkaza72 

Download your free Data Science - Career Transformation Guide.

For your listening-reading pleasure:

Explore our Data-Driven Organizations Audible and Kindle book series on Amazon:

1 - Data-Driven Decision-Making  (Audible) (Kindle)

2 - Implementing Data Science Methodology: From Data Wrangling to Data Viz (Audible) (Kindle)

Much career success, Lawrence E. Wilson - Financial Certification Academy (share with your team)