According to Harvard Business Review, Data Science is the sexiest job of the 21st century.
A recent report by Google concluded that since the last 18 months, the interest in Machine Learning has doubled.
Learn Data Science with Python Certification
Python programming, in the recent years, has become one of the most preferred languages in Data Science. And when it comes to building Machine Learning systems, Python provides an ideally powerful and flexible platform to build on. Through a comprehensive, hands-on approach, this course provides you the opportunity you need to experiment with a wide variety of Data Science and Machine Learning algorithms. We believe that a practical, hands-on approach is the key to meaningful learning and skills advancement. With this in mind, we integrate real-life exercises and activities throughout our trainings, with long-term retention of learning and development in mind.
By the end of this course, participants will have the leadership toolkit to shape and steer GenAI portfolios across their organization.
Strategic Application
- Data Science Introduction
- Data Science Project Lifecycle – CRISP-DM Model
- Data Science Toolkit
- Job outlook
- Prerequisite& Target Audience
- Introduction to Python, Anaconda, Spyder & Jupyter Notebook
- Installation & Configuration
- Basic Python Programming Concepts
- Data Structures in Python
- List
- Tuples
- Dictionary
- NumPy Array & it’s applications
- Control Structures
- Creating Custom Functions
- Exception Handling
- Random Variable
- Type of Random variables
- Discrete & Continuous
- Nominal
- Ordinal
- Interval
- Ratio
- Central Tendencies
- Mean
- Mode
- Median
- Measurement of dispersion
- Variance
- Standard Deviation
- Basic Statistics using NumPy
- Introduction to Probability Theory
- Probability Distribution Analysis
- Probability Mass Function
- Probability Density Function
- Normal Distribution
- Standard Normal Distribution
- Covariance & Correlation
- Pandas Dataframes & its applications
- Importing tables from RDBMS
- Analytics & Data Visualization using Matplotlib
- Univariate & Bivariate Statistical Analysis using Matplotlib
- Line Plot
- Area Plot
- Histogram
- Box Plot
- Scatter Plot
- Sampling Analysis
- Inferential Statistics
- Sampling Distribution
- Central Limit Theorem
- Hypothesis Testing
- 1 tail test and 2 tail test
- Type I and Type II errors
- P value
- Level of Significance
- Confidence Interval
- Statistical Analysis using Seaborn
- KDE Plot
- RegPlot
- Joint Plot
- Heatmap
- Data Sampling
- Simulating Normal Distribution
- Calculating PDF & CDF
- Hypothesis Testing – Case Study
- Introduction to Machine Learning
- Estimation Function
- Reducible & Irreducible errors
- Supervised & Unsupervised
- ML Algorithms ML Model Training & Testing
- Parametric & Non- Parametric Algorithms
- Regression Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Linear Regression methods
- Ordinary Least Square
- R Squared method
- Adjusted R Square
- Regression Evaluation Metrics – MSE,RMSE
- Bias & Variance
- Model Under fitting and Overfitting
- Feature Engineering
- Null Data Imputation Techniques
- Outlier Analysis
- Categorical Encoding
- Label Encoding
- One Hot Encoding
- Feature Selection Techniques
- Correlation Analysis
- Chi Square Test
- Machine Learning Case Study 1 – Multiple Linear Regression
- Logistic Regression
- Simple Logistic Regression
- Multiple Logistic Regression
- Logistic Regression Function
- ROC AUC Analysis
- Model Evaluation using Confusion Metrix
- Accuracy, Precision, Recall & Specificity
- Machine Learning Case Study 2– Multiple Logistic Regression
- Feature Scaling
- Addressing Imbalanced Data using SMOTE/MSMOTE
- Model Cross Validation using K- Fold Cross Validation Classification Analysis
- K Nearest Neighbor Classifier
- Decision Trees
- Classification and Regression Tree
- Random Forest
- Information Gain & Entropy
- Machine Learning Case Study 3 – Classification Analysis using KNN,
- Decision Tree & Random Forest
- Clustering Algorithms
- K Means Clustering
- Hierarchical Clustering
- Elbow Curve Graph
- Machine Learning Case Study 4 – Clustering Analysis using K-Means
- Clustering
- Recommendation Engines
- Collaborative filtering & Types
- Machine Learning Case Study 5 – Recommendation Engine using
- Collaborative filtering
hands-on learning model
Access to sanitized process maps, KPI definitions, candidate initiative lists, and basic cost baselines (time, cycle time, error or rework rates)
Speak with a Cognixia specialist about enrollment options, custom cohorts for your leadership team, or tailored delivery formats for your organization.
One of our specialists will contact you within one business day.
Asked Questions
Find details on duration, delivery formats, customization options, and post-program reinforcement.
We proudly seek out and employ the best in the industry! Our class is run by certified industry and subject-matter experts with complete and comprehensive experience under their belts
To attend the live virtual training, a speed of at least 2 Mbps would be required
You’ll have lifetime access to our Learning Management System (LMS), including class recordings, presentations, sample code, and projects. You’ll also be able to view recordings of each session. We also have our technical support team ready to assist you with any questions you may have.
of the future
Enroll your leadership cohort in Designing GenAI Use-Case Portfolios & Business Cases.
Custom cohorts available for enterprise teams.