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      • Data Science using Python

      Data Science using Python

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      Ramisha Rani K
      (1 review)
      ₹13,000.00 ₹999.00
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      DataScience

      Data Science is the study of data involving the development of models and methods to juice out the required result. There is an enormous professional opportunity accessible in the field of data science. The vast majority of the up-and-comers are searching for a worthwhile vocation in this specific area. Hence, data science preparation is significant for the freshers and for the experts who are searching for a Data researcher vocation. So as to look at and deal with a colossal arrangement of information through edge open source instruments and information examination calculations, it is required for the possibility to get sufficiently prepared in Data Science. These days there is satisfactory detail accessible which unmistakably shows that there is a shortage of ability to fill the prerequisites of data science experts.

      Are you excited to know these benefits?

      Read below

      What you will learn ?

      • Introduction to Artificial Intelligence

      Get introduced to the Buzz word Artificial Intelligence, Machine Learning and Deep Learning.

      • Tools 

      Anaconda, Spyder, Jupyter Notebook,Virtual Environment.

      • Basics of Python

      Object Oriented Programming Language, Basics of Python

      • Exploratory Data Analysis

      Explores the data from different perspectives and tunes them finely thereby giving a cumulative result of the main characteristics obtained by analyzing the data set.

      • Descriptive Data Analysis

      Brings out the basics of data and by which the same suggests gives the entire description of the data. Under Descriptive Data Analysis, you’ll study various measures, samples and the procedures to work with them in detail.

      • Inferential Data Analysis

      Specifies the inferences that can be made from and within the data. In easy words, Inferential Data Analysis sorts out the various methodologies in forecasting the data.

       
      • Data Pre-processing

      Technique used to transform the unstructured, messy and raw data into structured, neat and understandable format in a step wise manner.

      •  Data Visualization
      The most important part of Data Science is visualizing the hidden information of the data.
      Learn the data visualization using Matplotlib, Seborn and Tableau.

      Course Outcomes:

      • By the end of your happy learning of data science with us,
      • You’ll be able to build your programming skills.
      • You’ll be able to perform statistical analysis of data.
      • You’ll be able to construct your own models and analyze them in real-world contexts.
      • You’ll be confident enough to work with the algorithms and tools required for data science.
      • You’ll become a pro in data management.

      And what not? MUCH MORE!!!!!

      Aren’t you enticed to be a DATA SCIENTIST?

      Yes, right!!!

      We are here to lead you!!!!!

      Course Features

      • Lectures 107
      • Quizzes 0
      • Students 396
      • Assessments Yes
      CoursesData Science using Python
      • Course Overview
        • Lecture1.1
          Welcome Note 01 min
        • Lecture1.2
          The Three Set of Three Secret 05 min
        • Lecture1.3
          Download the Material
      • Introduction to Artificial Intelligence
        • Lecture2.1
          What is Artificial Intelligence? 05 min
        • Lecture2.2
          What is Machine Learning? 03 min
        • Lecture2.3
          What is Deep Learning? 01 min
      • Anaconda Installation
        • Lecture3.1
          Download the Anaconda 03 min
        • Lecture3.2
          Create New Virtual Environment 06 min
        • Lecture3.3
          Install the Required Library 03 min
        • Lecture3.4
          Install Jupyter Notebook 03 min
      • Basics of Python: Oops
        • Lecture4.1
          Introduction to Programming 06 min
        • Lecture4.2
          Overview about class 04 min
        • Lecture4.3
          What is Oops
        • Lecture4.4
          Access operator/Dot Operator 09 min
        • Lecture4.5
          Python Basics-List,Dataset 08 min
        • Lecture4.6
          Python String 03 min
        • Lecture4.7
          Python Variable 04 min
      • Exploratory: Descriptive Analysis
        • Lecture5.1
          Introduction to Data Science 09 min
        • Lecture5.2
          Basic Terminology of Data Science: Key Term -1 10 min
        • Lecture5.3
          Basic Terminology of Data Science: Key Term -2
        • Lecture5.4
          Types of Variable 08 min
        • Lecture5.5
          Concepts under Data Science 09 min
        • Lecture5.6
          Topics Under Descriptive Analysis 05 min
        • Lecture5.7
          Data Science: Project Report 04 min
        • Lecture5.8
          What is outlier? 04 min
        • Lecture5.9
          What is Mean and Purpose of it 08 min
        • Lecture5.10
          What is Median and Purpose of it 06 min
        • Lecture5.11
          Mean vs Median 07 min
        • Lecture5.12
          Mode 05 min
        • Lecture5.13
          Python: Dataset Explanation 10 min
        • Lecture5.14
          Python: Mean, Median and Mode 07 min
        • Lecture5.15
          Python: Quantitative and Qualitative 11 min
        • Lecture5.16
          Python Class and Function Fair copy 15 min
        • Lecture5.17
          Python Custom Table for Mean, Median and Mode 11 min
        • Lecture5.18
          Percentile and Purpose of it 11 min
        • Lecture5.19
          Python: Percentile and Lesser and greater range of outlier 11 min
        • Lecture5.20
          Inter Quartile Range and Outlier 10 min
        • Lecture5.21
          Python IQR 09 min
        • Lecture5.22
          Python IQR: Lesser range outlier and Greater range outlier 09 min
        • Lecture5.23
          Python Fair Descriptive 08 min
        • Lecture5.24
          Python Detecting Lesser and Greater outlier 09 min
        • Lecture5.25
          Python Detecting Outlier Fair copy
        • Lecture5.26
          Cross Checking for replaced outlier 11 min
        • Lecture5.27
          Frequency , Relative Frequency and Cumulative Frequency 10 min
        • Lecture5.28
          Python: Frequency, Relative Frequency and Cumulative Frequency 11 min
        • Lecture5.29
          Fair Frequency, Relative Frequency 08 min
        • Lecture5.30
          Variance 07 min
        • Lecture5.31
          Standard Deviation 09 min
        • Lecture5.32
          Python Standard Variance 05 min
        • Lecture5.33
          Skewness and Kurtosis 08 min
        • Lecture5.34
          Python Skewness, Kurt and Histogram 05 min
      • Exploratory: Inferential Analysis
        • Lecture6.1
          What is Inference Analysis? 04 min
        • Lecture6.2
          Univariate ,Bivariate and Multivariate 06 min
        • Lecture6.3
          Concept Under each Analysis 03 min
        • Lecture6.4
          Co-Variance 07 min
        • Lecture6.5
          Correlatoion 08 min
        • Lecture6.6
          Python Covariance, Correlation 14 min
        • Lecture6.7
          Multicollinearity 11 min
        • Lecture6.8
          Variance Inflation Factor and Homoscedasticity 06 min
        • Lecture6.9
          Homoscedasticity and Heteroscedasticity 06 min
        • Lecture6.10
          Introduction univariate 10 min
        • Lecture6.11
          Probability Density Curve 05 min
        • Lecture6.12
          Python PDF 14 min
        • Lecture6.13
          Python CDF 05 min
        • Lecture6.14
          Z-Score 11 min
        • Lecture6.15
          Python Z-Score 03 min
        • Lecture6.16
          Types of Test 06 min
        • Lecture6.17
          Paired & Unpaired T-Test 06 min
        • Lecture6.18
          Python T Test 05 min
        • Lecture6.19
          F Test 02 min
        • Lecture6.20
          Hypothesis Testing 15 min
        • Lecture6.21
          Analysis of Variance and One way Testing 10 min
        • Lecture6.22
          Python Anavo One Way Testing 02 min
        • Lecture6.23
          Anavo Two Analysis 05 min
        • Lecture6.24
          Python Anavo Two way Testing 05 min
        • Lecture6.25
          Python Two Post hoc 07 min
      • Data Visualization: Matplotlib
        • Lecture7.1
          Python_Matplotlib 09 min
        • Lecture7.2
          Python_Line Plot 02 min
        • Lecture7.3
          Python_Bar Plot 08 min
        • Lecture7.4
          Python_Histogram 05 min
        • Lecture7.5
          Python_Label 03 min
        • Lecture7.6
          Python 3D-Meshgrid 04 min
        • Lecture7.7
          Python Gradient 04 min
      • Data Visualization: Seaborn
        • Lecture8.1
          Python_Seaborn_color 03 min
        • Lecture8.2
          Dist & JointPlot 06 min
        • Lecture8.3
          Python-Pairplot 05 min
        • Lecture8.4
          Python Strip, Swarmplot 05 min
        • Lecture8.5
          Python VoilinPlot & BoxPlot 03 min
        • Lecture8.6
          Factorplot & Regression 05 min
        • Lecture8.7
          LMplot 04 min
      • Data Visualization: Tableau
        • Lecture9.1
          Download the tableau 01 min
        • Lecture9.2
          How to load the file in Tableau 03 min
        • Lecture9.3
          Bar Chart 04 min
        • Lecture9.4
          Problem Statement-1 04 min
        • Lecture9.5
          Problem Statement-2 08 min
        • Lecture9.6
          Save the worksheet 02 min
        • Lecture9.7
          Title for the chart 02 min
      • Data Preprocessing
        • Lecture10.1
          Pre-processing Introduction 06 min
        • Lecture10.2
          Dropping Unwanted the column 02 min
        • Lecture10.3
          Spelling mistake check 02 min
        • Lecture10.4
          Normalization 02 min
        • Lecture10.5
          Nominal One hot Encoder 04 min
        • Lecture10.6
          Ordinal Labelencoder 02 min
        • Lecture10.7
          Python Null Check 03 min
        • Lecture10.8
          Python Filling Null Values 03 min
        • Lecture10.9
          Converting nominal to numbers 03 min
        • Lecture10.10
          Python Categorical Imputer 01 min
      author avatar
      Ramisha Rani K

      I am Ramisha Rani K.I have completed my M.Tech. in Communication Engineering at VIT,(Chennai Campus),Chennai. l have secured Second Topper of the VIT. Due to my interest and passion I have towards Artificial Intelligence, I serving as a Data Scientist. I focusing much on the field of Python, Machine Learning, Deep Learning, Natural Language Processing and Data Science.

      Artificial Intelligence Trainer with four years of experience which allowed me to know and to discuss with Industry Trends, Market Analysis and Research oriented opportunities.

      Belive in"Dreaming Big!Daring to spare!! Amaze yourself!!!" connecting passionate, enthusiastic and smart people to help them to find the best platfom.

      I have trained nearly 5000+ students, 500+Faculities in this domain.

      Reviews

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      • Radha

        Excellent Course

        Those who want to learn from basic can learn from this course.

      • Overview
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      ₹13,000.00 ₹999.00

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