Name: Introduction To Financial Analytics Course with Python- Henry Harvin® in San Francisco | Henry Harvin
Uploaded: 2020-07-10 18:54:08
Description: Certified Python Financial Analyst' CPFA course is a focused 32-hours instructor-led training and certification program that equips participants to explore+analyze+solve financial sector problems using popular analytics tools such as Python & Advanced Excel.

Module 1: Analytics- General
Financial Analytics with Python (CPFA) Course Curriculum
- What is Analytics (BI, BA, Levels, etc)
- Why Analytics (Appl in various domains..)
- Different Roles in Analytics
- Tools and Techniques in Analytics
- Data Science, Data Mining, Statistics, Machine Learning, Supervised and Non-Supervised Techniques
- CRISP Modeling Framework
- Scales of Measurements
- Quiz Module 1
Module 2: Python Environment
This module will help the candidate to gain knowledge about the Python Environment. Learn about Ananconda setup and various IDEs, GIT, and more. Create and Manage Analytics/ML Projects
- Anaconda - Download & Setup
- IDEs - Jupyter, Spyder, PyCharm
- Git - Setup and Configuration with IDEs
- Creating and Managing Analytics/ ML Projects
- Quiz Module 2
Module 3: Basic Programming and Data Structures
This module will help the candidate with knowledge of basic programming and data structures. Gain extensive knowledge about Libraries, Numpy, pandas, Matplotib
- Basic Data Structures & Programming Constructs
- Libraries
- Numpy
- Pandas
- Matplotlib
- Quiz Module 3
Module 4: Data Manipulation and Descriptive
This module will guide the candidate with the knowledge of Data Manipulation and Descriptive summary. Know about Group summaries, crosstab, pivot, reshape data and manage missing values. Learn to manage indexes in Pandas, Scaling of data, and more
- Group Summaries
- Crosstab, Pivot and Reshape data
- Managing Missing Values
- Outliers Detection
- Various types of Joins, merge
- Managing indexes in pandas
- Partitioning data into train and test set
- Scaling of Data (useful for Clustering)
- Quiz Module 4
Module 5: Statistics
This module will guide the candidate through the basics of statistics in Marketing Analytics. Learn extensively about Hypothesis testing, Probability distribution, and Sampling Techniques
- Basic Statistics (mean, median, mode)
- Other Statistics (sd, var, quantile, skewness, kurtosis)
- Hypothesis Tests (t-test, Chi-sq tests, etc)
- Probability Distributions (normal, binomial, etc)
- Sampling Techniques
- Quiz Module 5
Module 6: Graphical Representation of Data
This module will guide you through the techniques of Graphical Representation of Data. Learn about the selection of graphs and types of graphs. Manage plot parameters and advanced graphs such as correlations, heatmap, mosaic, and more
- Selection of Graph
- Basic Graphs (histogram, barplot, boxplot, pie, etc)
- Libraries (matplotlib, seaborn, plotline)
- Managing plot parameters(size, title, axis, legend, etc)
- Advanced Graphs (correlation, heatmap, mosaic, etc)
- Exporting graphs
- Quiz Module 6
Module 7: Linear Regression
This module will guide you through the basic understanding of Linear Regression. Know about multiple linear regression and its libraries. Learn the metrics of Linear Regressions and its application & assumptions
- Simple Linear Regression
- Multiple Linear Regression
- Libraries - sklearn, statsmodel
- Predict DV on IVs
- Metrics of Linear Regression(R2, RMSE, p-values)
- Applications of Linear Regression
- Assumptions of Linear Regression
- Quiz Module 7
Module 8: Logistic Regression
This module will guide the learner with knowledge of Logistic Regression. Know the metrics of logistic regression. Predict the probability of DV on IV. Know extensively about applications of Logistic regression
- Difference between Linear and Logistic
- Logistic Regression
- Metrics of Logistic Regression (confusion matrix, ROC curve)
- Predict the probability of DV on IV
- Applications of Logistic Regression
- Quiz Module 8
Module 9: classification
This module will guide the candidate with the knowledge of classification in Marketing Analytics. Understand the tree from the plot and know about the classification tree. Learn to improve tree accuracy using random forests. Know the applications of the decision tree, KNN, Neural Networks, SVM, and more
- Difference between classification and regression decision trees from CART models
- Understanding tree from the plot
- Classification Tree - predict class, plot, accuracy
- Regression Tree - predict numerical value, plot, RMSE
- Improving tree accuracy using Random Forests
- Bagging and Boosting
- Applications of Decision Tree
- KNN (k-nearest neighbors)
- Neural Networks
- Gradient Descent
- SVM (Support Vector Machine)
- Quiz Module 9
Module 10: Cluster Analysis
This module will guide you through the knowledge of Cluster Analysis. Know about the Clustering for grouping data and its types. Learn about extracting data in clusters and application of clustering
- Clustering for Grouping Data
- Types - Hierarchical & Non-Hierarchical
- K Means - output metrics (iter, error, plot)
- Hierarchical (Agglomerative & Divisive) - Dendrogram, Visual plot
- Extracting the data in clusters, Cluster Centers
- Applications of Clustering
- Quiz Module 10
Module 11: Time Series
This module will guide you through the knowledge of the Time Series. Create and manage date & time and time-series data. Download stock prices data using libraries and analyzing them, Know about the time series analysis and application of time series analysis
- Creating & Managing dates and times
- Creating and managing time-series data
- Download stock prices data using libraries and analyzing them
- Time Series Components(Seasonal, Irregular, Trend) & Types (Additive & Multiplicative)
- Time Series Analysis - Simple Moving Average, Exponential Smoothing, ARIMA forecasting
- Plotting Time Series, Candlesticks diagram for stocks
- Applications of Time Series Analysis
- Quiz Module 11
Module 12: Complimentary Module 1: Soft Skills Development
- Business Communication
- Preparation for the Interview
- Presentation Skills
Module 13: Complimentary Module 2: Resume Writing
- Resume Writing

Know the complete offerings of our Financial Analytics with Python (CPFA) Course

Financial Analytics with Python (CPFA) Course in San Francisco

25 Nov 2024

32 Hours

25 Nov 2024

32 Hours

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Know the complete offerings of our Financial Analytics with Python (CPFA) Course in San Francisco

Key Highlights

32 Hours of Instructor-Led Sessions

11 Hours of Live Interactive Doubt Solving Sessions

6 Hours of Live Master Sessions by Industry Experts

192 Hours of Self-Paced Learning

192 Hours Hands on with Cloud Labs

Job-Ready Portfolio of 3 Capstone Projects

74 Auto-Graded Assessments

6+ Industry Case Studies

48 Guided Hands-On Exercises

3+ Assignments and Mini Projects

1 Mock Interview and 1 Hackathon

Get 1-Year Gold Membership of Henry Harvin® Data Science & Analytics Academy

Get a guaranteed Internship with Henry Harvin® and in top MNCs like J.P. Morgan, Accenture & many more via Forage

Get 3 in 1 Placement support through Placement Drives, Premium access to Job portal & Personalized Job Consulting

2 Hours of Mentorship by Industry Experts

Earn Certification of Course from Henry Harvin®, Govt of India recognized & Award-Winning Institute and NSDC Certification

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Access the best jobs in the industry

You Get 10-In-1 Program

About the Financial Analytics with Python (CPFA) Course in San Francisco

Financial Analytics

AGOTA™ Framework

Trainers at Henry Harvin®

Alumni Status

Gold Membership Program

Learning Benefits

Career Benefits

5+ Projects covered (Hands-on Practical Experience)

Curriculum For Financial Analytics with Python (CPFA) Course in San Francisco

Module 1: Analytics- General

Module 2: Python Environment

Module 3: Basic Programming and Data Structures

Module 4: Data Manipulation and Descriptive

Module 5: Statistics

Module 6: Graphical Representation of Data

Module 7: Linear Regression

Module 8: Logistic Regression

Module 9: classification

Module 10: Cluster Analysis

Module 11: Time Series

Module 12: Complimentary Module 1: Soft Skills Development

Module 13: Complimentary Module 2: Resume Writing

Admission Closes On 25 Nov 2024

Post Graduate Program in Analytics

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What you'll Learn in this course

Best Practices for Data Measurement & Analysis

Developing Business Strategies

Transactional Data Analysis

Preparing Financial Reports

Managing Tangible and Intangible Assets

Applying Analytics Tools & Techniques for Financial Analysis

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