Data Science with Python Course in Chandigarh Mohali
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Data Science Training Course in Chandigarh Mohali Panchkula

Data Science with Python Course Training in Chandigarh Mohali

ThinkNEXT Technologies Private Limited introducing Data Science in Python training Course in Chandigarh Mohali. Here, you will learn about influential ways to store and manipulate data as well as cool data science tools to start your own analyses.
ThinkNEXT provides Data Science Training in Chandigarh Mohali from certified experts. Our course helps you to learn various data analytics techniques using Python programming. Data Science Course content is designed by experts to match with the real world requirements for both beginner and advanced level.
If you are looking for Data Science in Python Training in Chandigarh Mohali, Enroll for the Free Demo Session.
By doing this course you become master in data science skills and will help you to handle an interview with more confidence if you are looking for a job in data science domain.

Data Science
Data science is multi-field technology inclusive of technologies like R, SAS, Hadoop, and Machine Learning to extract knowledge and insights in various forms related to data science. Enterprises and businesses need high-end tools and technology to drive business insights and perform critical data analysis, developing predictive models. Data Science helps businesses manage large sets of data, using different algorithms and mathematical analysis for extracting valuable insights to apply strategic decisions.
Data analytics is now a top priority for organizations. Data Science has come out as a new field with increased job opportunities and high pay scale as compared to other fields. Big analytics has been gaining momentum as we are generating new trends of data.

Data Science in Python Courses in Chandigarh Mohali
Python is a very superior programming language used for most of the applications. Eventually, this open source language has created quite a few tools to effectively work with Python. A large number of tools have been built specifically for data science. As a result, analyzing data with Python has never been simple.
Python is a general-purpose programming language that is becoming extremely popular for doing data science. Worldwide Companies are using Python to produce insights from their data and get a competitive edge. Data analytics certification courses focus on Python specifically for data science.

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Data Science Course Content:

Module1: Introduction

  What Data Science?
  Common Terms in Analytics
  Analytics vs. Data warehousing, OLAP, MIS Reporting
  Relevance in industry and need of the hour
  Types of problems and business objectives in various industries
  How leading companies are harnessing the power of analytics?
  Critical success drivers
  Overview of analytics tools & their popularity
  Analytics Methodology & problem solving framework
  List of steps in Analytics projects
  Identify the most appropriate solution design for the given problem statement
  Project plan for Analytics project & key milestones based on effort estimates
  Build Resource plan for analytics project
  Why Python for data science?

Module2:Core Python

  Overview of Python- Starting with Python
  Introduction to installation of Python
  Introduction to Python Editors & IDE's(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
  Python Syntax
  Variables & Data Types
  Operators
  Conditional Statements
  Working With Numbers & Strings
  Collections API
  LISTS
  TUPLES .
  DICTIONARY   
  Date and Time
  Function & Modules
  File handling
  Exception Handling
  OOPS Concepts in python
  Regular Expression

Module 3: Python Libraries for Data Science

  Numpy
  Scify
  pandas
  scikitlearn
  statmodels
  nltk

Module 4: Python Modules for Access, Import/Export Data

  Importing Data from various sources (Csv, txt, excel, access etc.)
  Database Input (Connecting to database)
  Viewing Data objects - subsetting, methods
  Exporting Data to various formats
  Important python modules: Pandas, beautiful soup

Module 5: Data Manipulation, Cleansing and Munging

  Cleansing Data with Python
 Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc.)
  Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)
  Python Built-in Functions (Text, numeric, date, utility functions)
  Python User Defined Functions
  Stripping out extraneous information
  Normalizing data
  Formatting data
  Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc.)

Module 6: Data Analysis and Visualization

  Introduction exploratory data analysis
  Descriptive statistics, Frequency Tables and summarization
  Univariate Analysis (Distribution of data & Graphical Analysis)
  Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
  Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc.)
  Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc.)
  Data visualization with tableau.

Module 7: Statistics

  Basic Statistics - Measures of Central Tendencies and Variance
  Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem
  Inferential Statistics -Sampling - Concept of Hypothesis Testing
  Statistical Methods - Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square
  Important modules for statistical methods: Numpy, Scipy, Pandas

Module 8: Predictive Modeling

  Concept of model in analytics and how it is used?
  Common terminology used in analytics & modeling process
  Popular modeling algorithms
  Types of Business problems - Mapping of Techniques
  Different Phases of Predictive Modeling

Module 9: Data Exploration for Modeling

  Need for structured exploratory data
  EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
  Identify missing data
  Identify outliers data
  Visualize the data trends and patterns

Module 10: Data Preparation

  Need of Data preparation
  Consolidation/Aggregation - Outlier treatment - Flat Liners - Missing values- Dummy creation - Variable Reduction
  Variable Reduction Techniques - Factor & PCA Analysis

Module 11: Solving Segmentation Problems

  Introduction to Segmentation
  Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
  Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
  Behavioral Segmentation Techniques (K-Means Cluster Analysis)
  Cluster evaluation and profiling - Identify cluster characteristics
  Interpretation of results - Implementation on new data

Module 12: Linear Regression

  Introduction - Applications
  Assumptions of Linear Regression
  Building Linear Regression Model
  Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
  Assess the overall effectiveness of the model
  Validation of Models (Re running Vs. Scoring)
  Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
  Interpretation of Results - Business Validation - Implementation on new data

Module 13: Logistic Regression

  Introduction - Applications
  Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
  Building Logistic Regression Model (Binary Logistic Model)
  Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
  Validation of Logist ic Regression Models (Re running Vs. Scoring)
  Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
  Interpretation of Results - Business Validation - Implementation on new data

Module 14: Time Series Forecasting

  Introduction - Applications
  Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  Classification of Techniques(Pattern based - Pattern less)
  Basic Techniques - Averages, Smoothening, etc
  Advanced Techniques - AR Models, ARIMA, etc
  Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc

Why Data Science with Python Training from ThinkNEXT

  Free Spoken English, Personality Development and Interview Preparation (HR+Technical) Classes on Daily basis so that students need not to struggle for jobs as a fresher
  Part Time / Full Time Job Offer for each student during training (Earn while you learn)
  Life-Time Validity Learning and Placement Card
  Data Science Training from top industry experts having more than 5 years of experience
  Data Science demo class is also offered by ThinkNEXT
  Data Science Training and Project Certificate By ThinkNEXT
  Data Science Experience Certificate by ThinkNEXT
  100% Practical, Personalized training with Live Projects
  Multiple Job Interviews + 100% Job Assistance
  Opportunity to work on live projects
  One-to-one Project and Project will be made Live and to make it Live, ThinkNEXT will provide sub-domain and hosting worth Rs. 3000/- absolutely free to each student for web based Project.
  Free Study Material
  Highest level of Infratsructure in Chandigarh Mohali Panchkula with 200+ computers and 16+ Labs




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