What is Data Science ?

Revanth Technologies provides the best Data Science with Python Online Training in India with industry leading experts. Data science is an interdisciplinary field that utilizes logical strategies, cycles, calculations and frameworks to separate or extrapolate information and experiences from boisterous, organized and unstructured information, and apply Data across a wide scope of use spaces.

The speeding up volume of Data sources, and in this manner Data, has made Data science is one of the quickest developing field across each industry. The Data Science lifecycle involves various roles, tools, and processes, which enables Data analysts to get exact insights. A Data Science project contains Data ingestion, Data storage and data processing, Data analysis and Communicate.

Many organizations understood that without an incorporated stage, Data science work was wasteful, unstable, and hard proportional. This acknowledgment prompted the advancement of Data science stages. These stages are programming center points around which all Data science work happens. A decent stage mitigates a large number of the difficulties of executing information science, and assists organizations with transforming their information into bits of knowledge quicker and all the more productively.

Data Science is a "concept to unify statistics, data analysis, informatics, and their related methods" in order to "understand and analyse actual phenomena" with data. Data Science uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge.

Revanth Technologies provides Data Science with Python online training in India. For more information on Data Science with Python online training and demo classes contact +91 9290971883 / 9247461324 or WhatsApp : +91 9290971883. Email ID : revanthonlinetraining@gmail.com

Data Science with Python Online Training Course Content


MODULE 1: Introduction To Python - Data Science


  • Installation of Anaconda setup (Data Science Development Environment)
  • Installation of Pycharm
  • Working with Python List , List operation , Functions
  • Python Tuple , working and functions
  • Sets and Dictionary -operations and Working with them
  • Python More on Strings
  • Python Dates and Times
  • More on functions
  • Advanced Python Lambda
  • List Comprehensions

MODULE 2: Data Analysis


1. Data Wandering

  • All about files Files
  • importing and exporting data with CSV files
  • XLRD module - working with xls .xlsx formats
  • Json data
  • XML data
  • Relational data Bases
  • Sql in python
  • Data quality Analysis

2. DATA MANIPULATION - Cleaning - Munging - Cleansing Data with Python

  • strong>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
  • Normalising data
  • Formatting data
  • Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

3. DATA 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)

4. DATA ANALYSIS WITH PANDAS

  • The Series Data Structure
  • Querying a Series
  • The Data-Frame Data Structure
  • Data-Frame Indexing and Loading
  • Querying a Data-Frame
  • Indexing Data-frame
  • Understanding business problem
  • Selecting columns from Pandas Data Structures
  • Treating with missing values, outliers, NaN values
  • Creating new columns
  • Aggregate data ( use: groupby, merge, pivot, lambda)
  • Identifying unique values in data
  • Filter Data
  • Using basic functionality of Pandas API

MODULE 3: Mathamatics


1. STASTISTICS

  • 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

2. PROBABILITY

  • Probability , Conditional Probability
  • Basic of Probability, Independent and Dependant events
  • Conditional Probability and Bayes Theorem
  • Continuous Probability Distributions
  • Mean, Median, Mode, Range
  • Determination of statistical techniques
  • Standard Deviation, Variance, Covariance, Correlation
  • outliners
  • Distribution of Data – Normal, Binomial, Gaussian
  • Different types of Data
  • Continuous , Categorical, Range
  • Testing of Hypothesis – which covers
  • Level of Significance (LOS), Level of Confidence, P-Value, T test, Z-test, ANOVA Test, CHI -Square Test

MODULE 4: Machine Learning


1. SUPERVISED LEARNING AND MODEL BUILDING

  • Process of Machine Learning
  • Model Building based on Data sets
  • Splitting Data: Training and Test sets
  • Regression Analysis (Linear, Multiple, Logistics Regression)
  • Classification concepts and Distance Functions
  • K-nn Algorithm concept and demonstration with data sets
  • Bayes Classification concept and demonstration with data sets
  • Decision Tree Algorithm concept and demonstration with data sets
  • Random Forests - Ensembling Techniques and Algorithms

2. UNSUPERVISED LEARNING AND MODEL BUILDING

  • Unsupervised Learning and Clustering Techniques
  • Centroid-based Clustering: K- Mean Algorithm concept and demonstration
  • Hierarchical Clustering concepts and Applications
  • Density-based Clustering: DBSCAN Algorithm concept and demonstration

3. DIMENSION REDUCTION TECHNIQUES

  • Dimension Reduction Introduction
  • Why Dimension Reduction Required
  • LDA (Linear Discriminant Analysis) concept and applications
  • PCA (Principle Component Analysis) concept and applications

4. TIME SERIES FORECASTING: SOLVING FORECASTING PROBLEMS

  • Introduction - Applications
  • Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
  • Classification of Techniques(Pattern based - Pattern less)
  • vBasic Techniques - Averages, Smoothening
  • Advanced Techniques - AR Models, ARIMA

5. DATA SCIENCE PROJECTS WITH DATA SETS

  • Applying different algorithms to solve the business problems and bench mark the results
Data Science Online Training from India

Enquiry Form

Other Related Courses

R Programming Online Training in Hyderabad India

R Programming Online Training in Hyderabad India

Read More
Advanced Data Science Online Training in Hyderabad India

Advanced Data Science Online Training in Hyderabad India

Read More
Data Visualization Online Training in Hyderabad India

Data Visualization Online Training in Hyderabad India

Read More
Machine Learning Online Training in Hyderabad India

Machine Learning Online Training in Hyderabad India

Read More
Python Online Training in Hyderabad India

Python Online Training in Hyderabad India

Read More