Big Data Analytics with Python Language
" Big Data Analytics is the Top Ranked job of the 21st century - It has exciting work and incredible pay".
In Python for Data Analysis course, we assume students are already familiar with Python programming and they will learn advanced Python techniques useful for load, wrangling, cleaning, transformation and visualization of data. You will learn about SciPy, Numpy, Pandas and matplotlib package in this course.
Business-Accelerate in SMAC LAB & Software Consultant at ECL Project of UNICEF SMAC LAB
There is no prerequisite knowledge. But if you have basic math skills and basic to Intermediate Python Skills is preferable.
I am from a non-technical background. Will I benefit from this course?
Yes, the course presents both the business and technical benefits of Big Data analytics and Data Visualization. The data mining and technical discussions are at a level that attendees with a business background can understand and apply. Where technical knowledge is required, sufficient guidance for all backgrounds is provided to enable activities to be completed and the learning objectives achieved.
Who will benefit from this course?
The booming demand for skilled data scientists across industries makes this course suited for all individuals at all level of experience. We recommend this data science training specially the following professionals:
- Software professionals looking for a career switch in the field of analytics
- Professionals working in field of Data and Business Analytics
- Graduates looking to build a career in Analytics and Data Science
- Anyone with a genuine interest in the field of Data Science
After completion of this training course, you will be able to:
This training has a clear focus on the vital concepts of business analytics and Python . By the end of the training, participants will be able to:
- Work on data exploration, data visualization, and predictive modeling techniques with ease.
- Gain fundamental knowledge on analytics and how it assists with decision making.
- Understand basic and advanced NumPy (Numerical Python) features
- Perform data analysis with tools in the Pandas library
- Manipulate, process, transform, merge and reshape large volumes of data
- Solve data analysis problems in web analytics, social sciences, finance, and economics
- Measure data by points in time, specific instances, fixed periods, or intervals
Training will be held in TechnoBD Web Solution's Pvt Ltd's Premises
Section 1: Intro to Course and Python Course Intro Note on Python.
Section 2: Setup Installation Setup and Overview IDEs and Course Resources iPython/Jupyter Notebook Overview
Section 3: Learning Numpy Intro to numpy Creating arrays Using arrays and scalars Indexing Arrays Array Transposition Universal Array Function Array Processing Array Input and Output
Section 4: Intro to Pandas Series DataFrames Index objects Reindex Drop Entry Selecting Entries Data Alignment Rank and Sort Summary Statistics Missing Data Index Hierarchy
Section 5: Working with Data: Part 1 Reading and Writing Text Files JSON with Python HTML with Python pip install beautifulsoup4 pip install lxml Microsoft Excel files with Python
Section 6: Working with Data: Part 2 Merge Merge on Index Concatenate Combining DataFrames Reshaping Pivoting Duplicates in DataFrames Mapping Replace Rename Index Binning Outliers Permutation
Section 7: Working with Data: Part 3 GroupBy on DataFrames GroupBy on Dict and Series Aggregation Splitting Applying and Combining Cross Tabulation
Section 8: Data Visualization Installing Seaborn Histograms Kernel Density Estimate Plots Combining Plot Styles Box and Violin Plots Regression Plots Heatmaps and Clustered Matrices
Section 9: Example Projects. Data Projects Preview Intro to Data Projects Intro to Data Project - Stock Market Analysis Data Project - Intro to Election Analysis Titanic Project
Section 10 : Regular Expression Basic Patterns Basic Examples Repetition Group Extraction
Section 11 : SciPy Introduction Basic functions Special functions Integration Optimization Interpolation Fourier Transforms Signal Processing Linear Algebra Sparse Eigenvalue Problems with ARPACK Compressed Sparse Graph Routines Spatial data structures and algorithms Statistics Multidimensional image processing File IO Weave
Section 12 : Exploratory analysis in Python using Pandas Introduction to series and dataframes Project - Loan Prediction Problem