Python with Data Science

Python for Data Science course from sniffer Search is aimed to provide best interactive learning with practical example with real time data .

STUDENTS ENROLLED

    At the end of  the course you will learn:

    Statistical technique and curve to minimize error in Data Science

    Python basic and Python Package for Data Science

    Machine learning algorithm understanding and plotting curve with data

    Implement data science algorithm on real time data and find the best algorithm to fit the data and get error narrow down or predicted output

    Why Learn from us:

    • 40 Hours of Live Online Training
    • 24X7 eLearning Access, and support over Email
    • Mentorship from Industry Experts
    • Instant Resolution of Doubts and Queries
    • Dedicated Support and Guidance
    • Course Completion Certificate from

    Job Assurance:

    • After completing the course you will be able to get the best of data science job as we take care of learning from concept to practical understanding with real time data.
    • Data Scientist skills has shortage of 2 million in India itself
    • Data Scientist are high paid professional among all technology
    • We provide list of companies hiring on data science and as well as we will forward  all competent candidate resume to prospect employer

     

    Course Curriculum

    Overview of Data Science:
    Skills required becoming a data scientist Source of Big Data Steps involved in Data Analytics 00:00:00
    Big Data, Data Analytics and Python
    Different sectors using Big Data Data Analytics and Python Python library and package for Data Science Advantage of using python for Data Science Data Wrangling and Data Exploration 00:00:00
    Data Wrangling and data Exploration
    Data Wrangling and Data Exploration Handle issues in Data Wrangling Model selection in Data Exploration 00:00:00
    Exploratory Data Analysis
    How to approach the data Focus on data Assumption EDA Technique (Quantitative and Graphical) 00:00:00
    Introduction of Statistics
    Inferential method Descriptive Method Discussion on Mean ,Median,Variance and Standard Deviation 00:00:00
    Statistical Analysis
    Range ,Frequency and Central Tendency Measures of central tendency Histogram (Graphical Representation) Bell Curve and Kurtosis explain with Graph Statistical Technique(Hypothesis Testing) Hypothesis Testing-Process and Steps Error type in hypothesis testing Perform Hypothesis testing on different data type Chi-Square Test 00:00:00
    Python Installation and Basic og Python
    Installation of anaconda Installation of Jupyter notebook Python Variables Python Strings,loops,conditions Programming construct of Python 00:00:00
    Python data Structure for Data Science implementation
    List with example Python method ,class and object Tuples,element access in tuples Example of tuples Slicing tuples Dictionary(access and modify Dictionary elements with example) Set with example 00:00:00
    : Python Package for Scientific computing - Numpy
    Class and basic operation of ndarray Accessing array elements Copy and view Numpy method for shape manipulation Linear Algebraic function 00:00:00
    Python library Scipy
    Scientific calculation with Scipy Scipy sub package-optimization Python package Pandas Example of how to create a series with pandas Vectorized operations in Series Create Data Frame from dictionary Handle missing values with example 00:00:00
    Machine learning:Each algorithm and machine learning technique will be followed by practical example with mainly python
    Supervised and Unsupervised learning Linear Regression with One Variable Linear Regression with Multiple Variables Logistic Regression 00:00:00
    Unsupervised Learning: k-Means algorithm for clustering Regularization Support Vector Machines 00:00:00
    Dimensionality Reduction Anomaly Detection Recommender Systems 00:00:00
    Neural Networks: Representation Neural Networks: Learning 00:00:00
    Image Classification using Deep Neural Networks
      A beginner friendly approach using TensorFlow 00:00:00

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