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IBM

Data Science in Python

IBM via Independent

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Overview

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  • Get hands-on project experience by working on a series of Data Science hackathons.
  • Live online instructor led interactive sessions.
  • Learn Data Science by working on industry problems along with industry experts.
  • Start coding from the first few minutes. No time wasted on slides and theory.
  • Build relationships with other industry professionals and get help with your career.

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Certification

IBM will email you the certificate on successfully completing the project. Highlight this certificate in your resume and LinkedIn profile.

Weekly 1-on-1 meetings

If you opt for the IBM track, you will get 8 one-on-one meetings with an experienced Data Scientist who will act as your mentor.

36 hrs Live Webinar

The live interactive sessions will be delivered through online webinars. All sessions are recorded. You have lifetime access to recorded videos and live classes and can re-attend a future class anytime you want.

30 hrs Lab & 24 hrs Project

You will be working on real case studies and solving real world problems. Assignments will be given to get you familiarized with many functions in the Python Data Science library.

Lifetime Access & 24x7 Support

Once you enroll for a batch, you are welcome to participate in any future batches free. If you have any doubts, our support team will assist you in clearing your technical doubts.

Money Back Guarantee

DeZyre has a 'No Questions asked' 100% money back guarantee. You can attend the first 2 webinars and if you are not satisfied, please let us know before the 3rd webinar and we will refund your fees.

How will this Data Science in Python Training Benefit me?

Prepare yourself for a career as a Data Analyst and Data Scientist. 

  • Live online faculty led training
  • Learn NumPy - foundation library for Data Science in Python
  • Learn SciPy - key algorithms core to Python's scientific computing
  • Learn Pandas - library for data analysis and manipulation
  • Learn Matplotlib - python module for visualization to make graphs, pie charts
  • Learn SciKit - python module for machine learning

Syllabus

Introduction to Python Programming

  • Introduction to Data Science
  • Introduction to Python
  • Basic Operations in Python
  • Variable Assignment
  • Functions: in-built functions, user defined functions
  • Condition: if, if-else, nested if-else, else-if

Data Structure - Introduction

  • List: Different Data Types in a List, List in a List
  • Operations on a list: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a List
  • Applying functions on a List
  • Dictionary: Index, Value
  • Operation on a Dictionary: Slicing, Splicing, Sub-setting
  • Condition(true/false) on a Dictionary
  • Applying functions on a Dictionary
  • Numpy Array: Data Types in an Array, Dimensions of an Array
  • Operations on Array: Slicing, Splicing, Sub-setting
  • Conditional(T/F) on an Array
  • Loops: For, While
  • Shorthand for For
  • Conditions in shorthand for For

Basics of Statistics

  • Statistics & Plotting
  • Seabourn & Matplotlib - Introduction
  • Univariate Analysis on a Data
  • Plot the Data - Histogram plot
  • Find the distribution
  • Find mean, median and mode of the Data
  • Take multiple data with same mean but different sd, same mean and sd but different kurtosis: find mean, sd, plot
  • Multiple data with different distributions
  • Bootstrapping and sub-setting
  • Making samples from the Data
  • Making stratified samples - covered in bivariate analysis
  • Find the mean of sample
  • Central limit theorem
  • Plotting
  • Hypothesis testing + DOE
  • Bivariate analysis
  • Correlation
  • Scatter plots
  • Making stratified samples
  • Categorical variables
  • Class variable

Use of Pandas

  • File I/O
  • Series: Data Types in series, Index
  • Data Frame
  • Series to Data Frame
  • Re-indexing
  • Operations on Data Frame: Slicing, Splicing (also Alternate), Sub-setting
  • Pandas
  • Stat operations on Data Frame
  • Reading from different sources
  • Missing data treatment
  • Merge, join
  • Options for look and feel of data frame
  • Writing to file
  • db operations

Data Manipulation & Visualization

  • Data Aggregation, Filtering and Transforming
  • Lamda Functions
  • Apply, Group-by
  • Map, Filter and Reduce
  • Visualization
  • Matplotlib, pyplot
  • Seaborn
  • Scatter plot, histogram, density, heat-map, bar charts

Linear Regression

  • Regression - Introduction
  • Linear Regression: Lasso, Ridge
  • Variable Selection
  • Forward & Backward Regression

Logistic Regression

  • Logistic Regression: Lasso, Ridge
  • Naive Bayes

Unsupervised Learning

  • Unsupervised Learning - Introduction
  • Distance Concepts
  • Classification
  • k nearest
  • Clustering
  • k means
  • Multidimensional Scaling
  • PCA

Random Forest

  • Decision trees
  • Cart C4.5
  • Random Forest
  • Boosted Trees
  • Gradient Boosting

SVM

  • SVM - Introduction
  • Hyper-plane
  • Hyper-plane to segregate to classes
  • Gamma

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