Financial Analytics with Python in Chennai

Introduction to Financial Analytics with Python in Chennai

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Take this introduction to Financial Analytics with Python course to uplift your skills | This basic course will help you understand the concepts of Analytics like Machine Learning, Data Science | Join the course today and learn fundamentals of Python | Learn with real world application & enhance your decision making skills.

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13 May 2024

Learning Period

32 Hours

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About the Financial Analytics with Python

Introduction to Financial Analytics with Python

Learn what Analytics is followed by Python Environment. This course will give you a basic knowledge of tools used and also an understanding of tools and techniques in Analytics. Learn one of the most in-demand skills for free with no prior knowledge.

Know the complete offerings of our Introduction to Financial Analytics with Python in Chennai

Financial Analytics with Python Curriculum

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

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