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Data Science Course With Python Machine Learning

Data Science with Python certification course for Freshers & Working Professionals. Become a Data Scientist in 3 Months | 50 Hrs of Blended Learning | 6 Industry Projects | Placement Assured Program<br>

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Data Science Course With Python Machine Learning

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  1. Data Science Course With Python Machine Learning www.apponix.com

  2. Data Science Course Objectives • Python full coding from scratch • Visualization with Python • Statistics - theory and application in business • Machine Learning with Python - 6 different algorithms • Multiple Linear regression • Logistic regression • Variable Reduction Technique - Information Value • Forecasting - ARIMA • Cluster Analysis • Decision Tree • Random Forest • Case studies on Machine Learning (18 case studies) • SQL queries(with Python) • Business Presentation of Technical Solution in-front of end client. • Robotic Automation(with Python) • CV Building activities • Interview preparation • Mock Interview sessions www.apponix.com

  3. Machine Learning with Python Data Science Course Syllabus • 1:Introduction to Python Programming Language • Introduction and Installation of Python software Python packages: Pandas, & Numpy • Concepts of Data frame Filtering • Loc and iloc for filtering Usage of Boolean in Filtering Appending • 3: More data handling using Python • Pivot Table of Excel in Python Grouping function • Learning of SQL queries using Python Grouping numeric data • 4: Additional functions of Python • Text functions • Data cleaning with efficient text functions Inbuilt String functions of Python Reshape functions of Python • 2: Data handling in Python • Handling of Missing values If else statement • Extra trick of using if else statement Removal of Duplicates • Frequency Distribution • Merging – Inner, Outer, Left and Right Binding and Appending • Descriptive Statistics • Inbuilt Numeric functions of R • 5: Statistic • Everything you want to know about statistics….Well sort of!! Mean, Median, Mode • Standard Deviation, Variance, Normal Distribution Hypothesis testing • T-test, Anova, Normality test www.apponix.com

  4. 6: Linear Regression • Predictive Analytics – Linear Regression Concepts of Linear Regression • Simple and Multiple Linear Regression Automatic Dummy Variables creation technique Model Validation parameters • Model Assumption testing • Splitting of data for Validation and testing • Business Case Study with real data to model in Python • 8: Logistic Regression • Predictive Analytics – Logistic Regression Concepts of Logistic Regression • Difference between Linear Regression and Logistic Regression Automatic Dummy Variables creation technique • Model Validation parameters Model Assumption testing • Splitting of data for Validation and testing • Business Case Study with real data to model in Python • 7: Linear Regression Practice Case Study • Participants will be asked to develop a Linear Regression model on a real life data, in presence of the instructor. Time given is 2.5 hours. Participants will be treated like an industry employee, but in terms of help certainly the instructor will not be as ruthless as the boss. After completion of the model (with the help of the instructor wherever it is required), the instructor will show how to present a model to a real life client. www.apponix.com

  5. 10:Time Series Forecasting • Time series forecasting: ARIMA • Difference between forecasting and prediction Concepts of time series data • Concepts of ARIMA • Descriptive analytics for ARIMA Development of model • Best model selection Forecasting with the best model Residual analysis • Business Case Study with real data to model in R software • Participants will be asked to develop a model in presence of the instructor. • 11: Cluster Analysis • Unsupervised Machine Learning with R Cluster Analysis: Concepts • Cluster analysis with R – K Means, Hierarchical etc. • 12: Decision Tree and Random Forest • Concepts of Decision Tree Decision Tree with Python Concepts of Random Forest Random Forest with Python www.apponix.com

  6. Salary expectation after completing Data Science course • As there is a growing demand for Data Science Engineers, the salary is also constantly increasing for Data Science skills, • As per payscale.com Average salary for Data Scientist is Rs 9,12,453 Per year. Career after Data Science course • Large number of IT companies spread across World, you should not have any doubt on shortage of Data Science jobs. IT technologies is growing and there a huge demand for Data Science engineers. Data Science has more than 70% of the markets share in terms of providing services. www.apponix.com

  7. Office Address of Apponix Technologies • Head Office - Bangalore • 306, 10th Main, 46th Cross, 4th Block Rajajinagar, Bangalore - 560010 • M: +91 8050580888 • info@apponix.com www.apponix.com

  8. www.apponix.com

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