IBM-Data-Science-Professional-Certification:用于整个IBM数据科学专业认证的学习资料...

   # IBM Data Science Professional Certificate <br> <p align="center"> <img src="https://raw.githubusercontent.com/Thomas-George-T/IBM-Data-Science-Professional-Certification/master/ibm.svg" title="IBM logo" alt = "IBM logo" /> </p> ## About this Professional Certificate Data science is one of the hottest professions of the decade, and the demand for data scientists who can analyze data and communicate results to inform data driven decisions has never been greater. This Professional Certificate from IBM will help anyone interested in pursuing a **career in data science** or **machine learning** develop career-relevant skills and experience. It’s a myth that to become a data scientist you need a Ph.D. Anyone with a passion for learning can take this Professional Certificate – **no prior knowledge of computer science or programming languages required** – and develop the skills, tools, and portfolio to have a competitive edge in the job market as an entry level data scientist. The program consists of 9 online courses that will provide you with the **latest job-ready tools and skills**, including open source tools and libraries, Python, databases, SQL, data visualization, data analysis, statistical analysis, predictive modeling, and machine learning algorithms. You’ll learn data science through hands-on practice in the IBM Cloud using real data science tools and real-world data sets. Upon successfully completing these courses, you will have built a portfolio of data science projects to provide you with the confidence to plunge into an exciting profession in data science. In addition to earning a Professional Certificate from Coursera, you'll also receive a **digital badge from IBM** recognizing your proficiency in data science. ## Applied Learning Project This Professional Certificate has a strong emphasis on applied learning. Except for the first course, all other courses include a series of hands-on labs in the IBM Cloud that will give you **practical skills with applicability to real jobs**, including: **Tools:** Jupyter / JupyterLab, GitHub, R Studio, and Watson Studio **Libraries:** Pandas, NumPy, Matplotlib, Seaborn, Folium, ipython-sql, Scikit-learn, ScipPy, etc. **Projects:** random album generator, predict housing prices, best classifier model, battle of neighborhoods Read more below: **Course Link:** [IBM Data Science Professional Certificate](https://www.coursera.org/professional-certificates/ibm-data-science) ## Instructors - Alex Aklson - Polong Lin - Romeo Kienzler - Svetlana Levitan - Joseph Santarcangelo - Rav Ahuja - SAEED AGHABOZORGI ## Specialization Overview | Sr. No | Course | |:------:|----------------------------------------------------------------------------| | 1. | [What is Data Science?](1.What_is_Data_Science) | | 2. | [Tools for Data Science](2.Tools_for_Data_Science) | | 3. | [Data Science Methodology](3.Data_Science_Methodology) | | 4. | [Python for Data Science and AI](4.Python_for_Data_Science_and_AI) | | 5. | [Databases and SQL for Data Science](5.Databases_and_SQL_for_Data_Science) | | 6. | [Data Analysis with Python](6.Data_Analysis_with_Python) | | 7. | [Data Visualization with Python](7.Data_Visualization_with_Python) | | 8. | [Machine Learning with Python](8.Machine_Learning_with_Python) | | 9. | [Applied Data Science Capstone](9.Applied_Data_Science_Capstone) | ## Resources #### Data Science Toolkit - [IBM Developer Skills Network](https://labs.cognitiveclass.ai/login?logout=true) : Data Science toolkit including JupyterLab, JupterNotebook, Apache Zeppelin, RStudio etc. in your browser. - [Google Colab](https://colab.research.google.com) : Practice Python in your browser and execute Machine learning Models with Google Colab. - [Online Notebook viewer](https://nbviewer.jupyter.org) : View jupyter notebooks online. - [Foursquare API](https://developer.foursquare.com) : Foursquare API developer credentials portal. - [ArcGis](https://developers.arcgis.com/labs/python/search-for-an-address/) : Search for an address with Python. #### Useful Functions - [Check for NaN in Pandas DataFrame](https://datatofish.com/check-nan-pandas-dataframe/) - [Pandas get dummies or One Hot encoding](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.get_dummies.html) - [Rename a column in Pandas in Python](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.rename.html) - [Data cleaning with Pandas](https://towardsdatascience.com/data-cleaning-with-python-using-pandas-library-c6f4a68ea8eb) - [RStudio](https://cran.rstudio.com) - [RStudio package: Shiny](https://shiny.rstudio.com/) - [RStudio package: leaflet](https://rstudio.github.io/leaflet/) - [Importing JSON and HTML into pandas](https://www.datacamp.com/community/tutorials/importing-data-into-pandas) #### Useful Resources - [End to End Machine learning library](https://e2eml.school/blog.html#skills) - [Beginning with Exploratory data Analysis (EDA)](https://nbviewer.jupyter.org/github/Tanu-N-Prabhu/Python/blob/master/Exploratory_data_Analysis.ipynb) - [In depth Exploratory data Analysis (EDA)](https://www.kaggle.com/lalitharajesh/iris-dataset-exploratory-data-analysis) - [K-means Clustering](https://nbviewer.jupyter.org/github/temporaer/tutorial_ml_gkbionics/blob/master/2%20-%20KMeans.ipynb) #### Building Portfolio and Real world Experience - [Building an effective Data science Portfolio](https://towardsdatascience.com/how-to-build-an-effective-data-science-portfolio-56d19b885aa8) - [Getting real life Data science experience](https://towardsdatascience.com/3-ways-to-get-real-life-data-science-experience-before-your-first-job-545db436ef12) - [How not to build a data science project](https://towardsdatascience.com/how-not-to-build-a-data-science-project-baa494d98da4)