Data Science using Python

About Profound Data Science Classes?

According to the survey data scientist is the top ranking professional in the market. As a Data Scientist you need to understand the Business problem, gather data for the same and analyze by applying correct algorithms and techniques using current tools. A after running data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report.


Need of Data Science
  • Data Science is basically used to take better decision.
    For example, whether to buy Self Driven Car or not. Current Data analysis shows it will root out more than 2 million deaths caused due to car accidents
  • Predictive Analysis
    What Will Happen Next
  • Pattern Discovery
    Is there any hidden information in the data. For example, to analyse patterns of sales that is, in which month it is increasing

What is Data Science?
  • Asking the right Questions and Exploring the data
    Basically, you need to know what problem you are solving that is asking the right question. After asking questions you will have data for that as input and you will perform certain action on it
  • Modeling the data using various algorithms
    Suppose if you need to perform machine learning you have to decide which Model to use and which algorithms to use and then you need to train the model and so on.
  • Finally Communicating and Visualizing the results.
    After running the data through the Model you come out with the result of the exercise which include visualizing the result and communicates results to concern people in form of powepoint or some sort of report. All the information that has been gathered

Why learn Data Science course at Profound Edutech?
  • Data Science Course at profound is designed to train students and professional in the industry's most widely sought after skills, and make them job ready in the field of Data Science.
  • We at profound Edutech provide you with an excellent platform to learn and explore the subjects from Industry experts.
  • 100 % placement assurance for the trained candidates.
  • Trainers with more than 10+ years experience
Duration: 3.5 months
Eligibility: IT Professionals / Exposure to Information Technology
  • What is Data Science?
  • Why its important?
  • Life Cycle of Data Science
  • Role of a Data Scientist
  • Difference between Data Analytics & Data Science
  • Perform basic spreadsheet tasks
  • viewing, entering & editing data
  • moving, copying data
  • Cleaning & wranggling data
  • Conditional Formatting
  • Functions
    • CONCATINATE, LEN, TRIM
    • COUNTA, AGGREGATEIFS
    • SUMIF, COUNTIF
  • Fundamentals of analyzing
    • filter and sort data
  • Pivot Tables
    • HLOOKUP, VLOOKUP
  • What-if analysis
  • Charts
  • Descriptive Statistics
    • Annova, Regression
  • Mini Project on Excel
  • Create DB, Drop DB
  • Create Table, Drop Table, Alter Table
  • Data Types
  • Constraints, Not Null, Unique
  • Primary Key, Foreign Key
  • Create Index
  • Dates
  • Views
  • Built in functions
  • Summarizing results using group functions
  • Joins
  • Retrieving Data with Sub Queries
  • Manipulating Data
  • Mini Project on SQL Queries
  • Introduction to Python Programming
  • Installation & working
  • Basic Operators, Data types, Variables
  • Control Statements
  • Conditional Looping
  • Functions
  • Collections in Python
  • Object Oriented Programming
  • Modules and Packages
  • String
  • File Handling
  • Exception Handling
  • Multi-threading
  • Regular Expressions
  • Mini Project on Core Python
  • Numpy
  • Introduction, installation
  • 1D & 2D arrays
  • Array indexing – slicing & advance
  • Operations – Arithmetic, Logical
  • Math, String, Statistical
  • Set, Broadcasting
  • Pandas
  • Introduction, installation
  • Series – Creation, indexing
  • Slicing, attributes & functions
  • Dataframes – Creation
  • Merging dataframes,
  • Concatenate dataframes
  • Binary operations
  • Data input and output
  • Matplotlib
  • Introduction, installation
  • Data Visualization
  • Plots – single line, multiple line
  • Grid axes
  • Labels, color line markers
  • Seaborn
  • Distribution Plots
  • Category Plots
  • Matrix Plots
  • Grids, Regression Plots
  • Introduction to Plotly, Altair, ggPlot
  • Introduction to Plotly, Altair and ggplot
  • Introduction Microsoft Power BI Desktop
  • Connecting & Shaping Data
  • Creating a Data Model
  • Calculated Fields with DAX
  • Visualizing Data with Reports
  • Artificial Intelligence & Microsoft Power BI
  • Power BI Optimization Tools
 
  • What is Machine learning?
  • Machine Learning Methods
  • Predictive Models
  • Descriptive Models
  • Steps used in Machine Learning
  • Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Bias-Variance trade-off
  • Classification
  • Logistic Regression
  • K-Nearest Neighbors (K-NN)
  • SVM
  • Decision Trees
  • Random Forest
    • Clustering
    • K-means
    • Hierarchical
    • DBSCAN
    • Dimensional Reduction
    • Linear discriminant analysis
    • Principal component analysis
  • Neural Networks
  • Introduction to Neural Networks
  • Back propagation
  • Maths of neural networks
  • Conventional Neural Networks (CNN)
  • Introduction to Image processing
  • Basic convolution
  • Convolution Neural Network Application
  • Fine tuning
  • Recurrent Neural Networks (RNN)
  • Recurrent Neural Networks
  • Application in Time series & text Analytics
  • Convolution Neural Network Application
  • Fine tuning
  • NLP with Deep Learning
  • Introduction to NLP
  • Text representation with DL
  • Text classification
  • Grammar detection
  • Sentiment analysis
 
  • Mock test on mnc pattern
  • Coding test
  • Technical interview
  • Group discussion
  • Presentations
Project: Design, Development

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