Overview
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.
What You'll Learn
Designed with the industry’s most in-demand skills in mind, this course provides a solid foundation in Data Science and Machine Learning with Python expertise, helping you to ensure a promising career ahead. Our Data Science & Machine Learning with Python course includes all of the following:
- Introducing data science, with a focus on the job outlook and market requirements
- Data Science Project Life Cycle
- Basics of Statistics – Measures of Central Tendency and Measures of Dispersion
- Discrete and Continuous Distribution Functions
- Advanced Statistics Concepts – Sampling, Statistical Inference and Testing of Hypothesis
- Introduction of Python Programming, Anaconda and Spyder
- Installation and Configuration of Python
- Control Structures and Data Structures in Python
- Hands-on Applied Statistics Concepts using Python
- Functions and Packages in Python
- Graphics and Data Visualization Libraries in Python
- Introduction to Machine Learning
- Machine Learning Models and Case Studies with Python
- Software developers and programmers who want to reap the benefits of a lucrative Data Science and Machine Learning career
- Data Analysts or Financial Analysts from the non-IT industry who want to make a transition to the IT industry
- Individuals, students and corporate professionals who want to upgrade their technical skill set
Curriculum
- 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
Course Description
To define it simply, deriving insightful and meaningful information from large volumes of complex data is data science. In order to do this, data science relies on different field like statistics, computation, etc. The insights derived from different types of data help organizations make informed decisions and adds value to business processes.
Python is one of the most popular programming languages in the world today. It is an interpreted, object-oriented, high-level programming language, having simply syntax with numerous useful support modules, packages and libraries that encourage program modularity and code reuse.
According to recent reports, India would have more than 1.5 lakh new job openings in the field of data science. This is approximately 62% higher than the number of data science openings that arose in 2019. Most of these openings are expected to need professionals with less than 5 years of experience. Sectors like BFSI, energy, pharmaceuticals, eCommerce, etc. are the top employers for professionals skilled in data science.
Cognixia’s data science training begins with an introduction to data science. It goes on to discuss the data science project lifecycle, discrete and continuous distribution functions in data science, as well as basic and advanced concepts of statistics that are commonly used in data science. It also covers an introduction to the Python programming language, how to install and configure Python, control structures and data structures in Python, hands-on exercises on applied statistics concepts using Python, discussions on functions and packages in Python, graphics and data visualization libraries n Python. This data science training also covers an introduction to machine learning, machine learning models as well as important case studies in Python application.
Yes! Upon successful completion of this data science training, you will be required to appear for an assessment – a quiz or a test as you call it. Once you clear this assessment, you will be awarded with a globally recognized data science certification by Cognixia. This certificate will validate your skills and knowledge in the field of data science. You can share it on LinkedIn, add the credentials to your resume and use it as a supporting document to present your skills to potential or current employers.
This data science certification course is highly recommended for software developers and programmers, data analysts, financial analysts, or anyone interested in acquiring skills and knowledge in the field of data science.
In order to participate in this data science training course, participants need to have a basic understanding of computer programming languages. It would also be beneficial to have a fundamental understanding of statistical concepts, though it is not mandatory.
FAQs
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.
